Programming Languages Mentoring Workshop (PLMW)

Posted by hh360

Good morning from POPL 2015 in Mumbai, India.

Throughout this blog, * denotes a notation heavy segment in the talk. These can be difficult to summaries quickly without typesetting. See the authors paper online for now and I'll try to find speaker slides at the later time.

PLMW Organizers - Intro

This is the 4rd PLMW workshop and we are very pleased to see you all. Mentoring workshops are now being much more common. This year we have funded 75 students. We would like the thank the speakers, sponsors and organisers (including Annabel :)

Video from Derek

POPL proceedings are now available online


Nate Foster (Cornell University) - You and Your Graduate Research


- doing a PhD is not easy, many do not finish. This is a survivors story
- some of my comments many be US specific
- acknowledgements (including Jorge Cham from PhD comics :))


THis is useful from everyone, not just undergraduates.

Why you shouldn't do it?
- money: nick shows a graph of salaries in university, football coaching is clearly the right way to go for a big salary.
- start-ups: Tech start-ups are now a top topic, Oculus VR example and you don't need a PhD for this
- respect: Education is highly valued and there's respect from degrees, but no one will call you doctor exact your mom.
- to stay in school: Undergraduate was fun so lets stay in school, a PhD program is totally different.
- being a professor: Let all become a professor, demonstration of the numbers of academics verse other careers

Why you should?
- opens opportunities
- cool jobs in industry: leadership, work on cool problems, you can work on large systems and with real users
- freedom: applies on any level, freedom to choose interesting problems, opportunities to work on the big problems

What is a PhD?

Comparing degrees at different levels:
- high school: basics for life
- undergrad: broad knowledge in a field
- professional: advanced knowledge and practical skills
- PhD: advanced knowledge and a research contribution.

PhD is open edge

PhD is a transformation - we start with intelligent people who aren't researchers. Its a apprentice based scheme to being a researcher. Its not an easy or painless process.

PhD Success

pick an institution
The community that your in will shape your work,
Very important factors include advisor, opportunities and peers. Typically less important factors are finances, institution and location.

Dive into Research
Don't be distracted by other things e.g. classes and teaching
Pick great problems

"It is better to do the right problem the wrong way than the wrong problem the right way" - Hamming

Stay Engaged
Working with others means it easier, as you can motivate each other.
Know when its time to switch topics - good research are versatile and able to switch quickly
Independence - you need to know when the time is right to go rogue and ignore your adviser
Reaching escape velocity to graduate


Peter Müller (ETH Zürich) - Building automatic program verifiers

verification: Given a program P and a specification S, prove that all execution of P satisfies S
automatic verification: Develop an algorithm that decide whether P satisfies S

We can't prove halting => we are finished :)

Lets start again ...

Peter demonstrates of verification tool called Viper, using the typical account balance and transfer example. The verifier gives two error, we add preconditions and the verifier successes. We then use the verifier for thread safety, again fixing a concurrency bug with locking.

Automatic verification is not b/w. We can have semi-automatic verification: we can guide the verifier. The complexity of the code varies greatly, most success in the area focuses on a small area. We build verifiers from weaker guarantees. Verification can working with modularity (or maybe it will not). Varying complexity of properties.

7 Stage process

Define the research goals
What is the state of the art?
Find examples the area in the problem space that is not currently handled (well) by current verifiers.

Find Reasoning Principle
How to explain the correctness of the code to a friend? For the loop, we would use a loop invariant.
This is very different to techniques like model checking, I wouldn't say to friend "I have checked 15 billion possible evaluation paths and are all valid".

Break Down Arguments
Decompose the correctness argument: what do I need to prove for each program and what can I reuse between programs. How can I modularise the program.

Design Specification Language
Designing a language to express intended behaviour, we must know who the user will be and their level of experience. Find the right compromise between expressibility and simplicity, that is right for the intended audience.

Design Program Logic
Determine which properties need to be checked and which properties may be assumed.

Automate Proof Search
Utilize existing infrastructure like SAT and SMT solvers. Develop decision procedures for aspects not already covered by existing infrastructure.

Evaluate the Solution
Firstly an experimental evaluation, does the solution work on the example code you were executing. Then he meta-theory like the soundness proof.

Research Direction
Either develop now technical for new languages and feature (e.g. the recent interest in event-bases programming due to android.) or work in general infrastructure for utilise in many areas.


Frank Pfenning (Carnegie Mellon University) - Proof theory and its role in programming language research

I will show you why every PL researcher needs a bit of proof theory.

How do we write correct programs ?
We don't :)

In practice, programming and informal reasoning go hand in hand, we use mental logical assertions, e.g. after calling sort the list is sorted. It's vital to decompose the problem into part so we can reason locally.

We need to develop programming language with the logic of the programmer in mind. Think about your least favourite programming language and why the operational or logical model of the languages doesn't agree with you. Reasoning is an integral part of programming. We need to co-design the language and logic for reasoning about it.

Logic is computation so the key is to design coherent logical and operations semantics.


Computation first...
- runtime code generation => IS4
- partial eval => temporal logic
- dead code ele => model logic
- distribution computation => IS5
- message-passing
- concurrency => linear logic
- generic effects => lax logic

... and the logics first
- lax logic => ??
- temporal logic => ??
- epistemic logic => ??
- ordered logic => ??

Key ingredients.
Understand the difference between judgements and propositions. The basic style of proof systems e.g. natural deduction, sequent calculus, axiomatic proof system and binary entailment.

Example: Hypothetical Judgement*

Example: Runtime Code Generation

We have the source expression at runtime. We distinguish ordinary variable which are bound to values and expression values which are bound to source code.*


Stephanie Weirich (University of Pennsylvania) - How to write a good research paper

Stephanie is giving the popular talk "How to write a good research paper" from Simon Peyton Jones.

Start by writing the paper. It focuses us. Write a paper about any idea no matter now insignificant it seems. Then you develop the idea.

Identify your key idea, the paper main goal is to convey your idea. Be explicit about the main idea for the paper, the reviews should need to be a detective.

The paper flow:
- Here is a problem
- It's an interesting problem
- It's an unsolved problem
- Here is my idea
- My idea works

The introduction - describe the problem and what your paper contributes towards to problem. Don't describe the problem to broadly to quickly. It's vital to nail the exactly contributions.

The related work - it belongs after the main body of the paper, not straight after the introduction. Be nice in the related work. Be honest about the weaknesses of your approach.

Use simple, direct language, putting the reader first. Listen to readers.

Damien Pous (CNRS, LIP, ENS Lyon) - Coinductive techniques, from automata to coalgebra

Checking language equivalence of finite automata

Demonstration of the naive algorithm to compare equivalence by walking through the stages and comparing. This algorithm is looking for bi simulation. This algorithm has quadratic complexity. Instead we can used HK (Hopcroft and Kerp) algorithm.

[I got a bit lost in the theory of coalgebras for the rest of the talk, sorry]*


OCaml 2014

Posted by Leonhard Markert


Many submissions this year so a few had to be squeezed into "short talk" slots.

Session 1: Runtime system

Multicore OCaml, by Stephen Dolan (presenting), Leo White, Anil Madhavapeddy (University of Cambridge)

Stephen Dolan presenting Multicore OCaml

Stephen Dolan presenting Multicore OCaml

Still work in progresss. Concurrency v. parallelism. Concurrency: for writing programs (e.g. handle 10000 connections at once). Parallelism is for performance (e.g. making use of 8 cores).

LWT and Async give good support for concurrency (monads are a bit awkward).

Direct-style threading library: vmthreads and systhreads are not very efficient.

Parallelism: sad story. Can use multiple processes with manual copying ...

Unifying the two? -- concurrent programs are easily parallelized: should we use the same primitives?Java, C# and others do but it's a bad idea. At scale, death by context-switching.

Fibers for concurrency (cheap! -- have millions) and Domains for parallelism (expensive! -- have ~#core ones)

Concurrency primitives that are proposed are non-monadic. MVar: a blocking variable. Can use MVars to define an "async" function.

Demo using a Fibonacci function: One recursive call is done using "async". Parallelize it to 48 cores -- get diminishing returns as expected, but the speedup is impressive.

Q: Do you keep spawning even when no domains are free?
A: Yes. Spawning creates a new fiber which is extremely cheap (minor allocation of 30 words).

Q: Can you manipulate domains from within the program?
A: Yes.

Multiple domains run in parallel; fibers are balanced between domains. Creating domains is heavyweight.

OCaml is very fast for immutable data and functional programs -- try to keep this unchanged. Mutability is more complicated in a multithreaded system! Use a descendant of Doligez-Leroy.

Each domain has a private minor heap; no pointers exist between minor heaps. The major heap is shared. The major heap can point into minor heaps. Can do completely independent minor collections. Shared heap: mostly-concurrent, inspired by VCGC.

C API -- some minor changes required (sed can fix most!), e.g. caml_modify(&Field(x,i), y) becomes caml_modify_field(x, i, y). Atomicity guarantees of current GC are preserved.

Status: bootstraps, but GC needs more testing, tuning and benchmarking. Bytecode only at the moment. Weak references, finalizers etc. are still missing.

Q: Are you exposing a raw memory model?
A: We'll probably settle on enforcing an ordering of stores by using a memory fence on platforms which don't support this natively.

Q: Your collector stops all of the mutator sets. Is there any way of doing collection in parallel with mutators?
A: Have collection in parallel, then stop the world to verify (?)

Q: If fiber does a blocking system call, does that block the entire domain?
A: Yes at the moment, but there are plans to change that.

Q: Can what you are doing be used in monadic libraries in LWT or Async?
A: Yes but that would probably not a good idea because users of these libraries don't assume that their code is running concurrently.

Q: Is it possible to make sure that a particular fiber is in the same or is not in the same domain as other fibers?
A: Priorities, affinities to domains etc. are very interesting and we are just starting to provide these.

Ephemerons meet OCaml GC, by François Bobot (CEA)

François Bobot presenting "Ephemerons meet OCaml GC"

François Bobot presenting "Ephemerons meet OCaml GC"

Memoization -- computing a function with a cache while avoiding memory leaks: if a key is not needed anymore, we want to remove the entry from the cache. Particular case: heap-allocated keys not needed anymore means not reachable (apart from the cache).

Naive solution: use traditional dictionary data structure (has table, balanced tree etc.) -- problem: no key-value combination is ever released until the function is released!

Weak pointers -- a value not yet reclaimed can be accessed via a weak pointer. GC can release a value that is only pointed to by weak pointers.

Finalizers: can attach one or several to a value. So next idea; don't use K directly to index in K. But what if K and V are the same? Gives circular dependency, and nothing ever gets released.

Next idea: keep table in key! (Haskell: can do something like that with System.Mem.Weak) But we can still get cycles.

Ephemerons (Hayes 1997): value v can be reclaimed if its key k or the ephemeron can be reclaimed.

Implementation: OCaml runtime modified (Github pull request 22). Adds a new phase, cleaning, between mark and sweep.

Can use ephemerons to implement weak tables.

Session 2: Tools and libraries

Introduction to 0install, by Thomas Leonard (University of Cambridge)

Thomas Leonard presenting 0install

Thomas Leonard presenting 0install

Converted 0install from Python to OCaml. Will give intro to 0install -- decentralized, cross-platform package manager -- and report his experience porting Python code to OCaml code.

0install ( created in 2003. Make one package that works everywhere. Packages can come directly from upstream (or from distribution); switch to latest version easily.

Example: 0install add opam

Q: How does it deal with libraries?
A: Libraries are shared.

Porting to OCaml.

Python problems -- too slow (mobile platforms), too unreliable -- no static type checking, Python 3 trouble, PyGTK breakage...

Idea to port to a different language -- lots of languages compared (for 0install, only startup time matters)

OCaml: Bad -- top-level no readline support, syntax errors hard to find, unhelpful errors; Good -- fast!, stable, reliable.

Suggestion: OCaml for Python, Java, ... users on the website.

Porting process: used old Python and new OCaml parts together; communication via JSON. Literal translation first; refactor later in OCaml.

Results: similar LOC, 10x faster, reliable, great community!

Q: Can 0install deal with multiple versions of the same package?
A: For OCaml packages, use OPAM.

Q: How often did you run into type errors when porting to OCaml?
A: Porting the SAT solver was a little tricky. None and null are handled better in the OCaml code and all kinds of network errors are handled now.

Q: Are you still using objects?
A: Currently getting rid of objects and making things more immutable.

Transport Layer Security purely in OCaml (*), by Hannes Mehnert (presenting, University of Cambridge), David Kaloper Meršinjak (University of Nottingham)

Hannes Mehnert presenting a new TLS implementation in OCaml

Hannes Mehnert presenting a new TLS implementation in OCaml

Current state: Mirage OS -- memory safe, abstract, modular. But for security call unsafe insecure C code??

Motivation: protocol logic encapsulated in declarative functional core; side effects isolated in frontends; concise, useful, well-designed API (should be easy in comparison to OpenSSL!)

TLS: secure channel between two nodes. Most widely used security protocol family. Flexible algorithm: negotiation of key exchange, cipher and hash.

Detailed properties: authentication, secrecy, integrity, confidentiality, forward secrecy. Divided into protocol layers: handshake, change cipher spec, alert, application data, heartbeat subprotocol.

Authentication (X.509): client has set of trust anchors (CA certificates); server has certificate signed by a CA; during handshake client receivers server certificate chain; client verifies that server certificate is signed by a trust anchor.

Handshake demo using the OCaml TLS implementation.

Stats: 10kloc (compare OpenSSL 350kloc); interoperable. Missing features: client authentication, session resumption, ECC ciphersuites. Roughly 5x slower than OpenSSL. Took ~3 months to implement.

Future: implement missing features, improve performance, test suites, integration with Mirage.

More at

Q: Who will use this?
A: The University of Cambridge.

C: The team that discovered the Heartbleed bug ran their test framework against this library, and found five bugs in Linux but none in this library.

Q: What about timing and interaction with GC?
A: Best practice is not to use and data-dependent branches and to use the same memory access patterns. No data dependent allocations. But the preferred way is to do this in the language.

Q: How about tests?
A: We have a test suite but we'd like to automatically generate tests and run them against other implementations to be able to do comparisons.

OCamlOScope: a New OCaml API Search (*), by Jun Furuse (Standard Chartered Bank, Singapore)

Jun Furuse presenting OCamlScope

Jun Furuse presenting OCamlScope

Haskell has type classes, purity, laziness where OCaml is different. But what's really different is library search: Hoogle. Can search by name, by type, or both.

OCamlBrowser, OCaml API Search are very limited so Jun built OCamlScope.

OCamlBrowser works only for locally compiled code, uses OCaml typing code. OCaml API search was based on OCamlBrowser but has been discontinued.

Previously there were many problems with trying to build an OCaml API search. Now cmt/cmti files give compiled AST with locations, and OPAM gives unified installations; compiler-libs make it easier to access OCaml internals.

OCaml Scope: remote (hosted); search by edit distance.

525k entries (values, types, constructors) -- 115 OPAM packages, 185 OCamlFind packages. Note: need to deal with two different package systems! Scraping cmt/cmti with OPAM, create module hierarchy with OCamlFind; must detect OPAM - OCamlFind package relationships.

Future work: real alias analysis (instead of ad-hoc grouping).

Find it at

Q: Do you plan to integrate this with an editor?
A: That would be nice, sure.

C: Could use something like your edit distance approach to improve error message and have the compiler give suggestions.

Session 3: OCaml News

The State of OCaml (invited), Xavier Leroy (INRIA Paris-Rocquencourt).

Xavier Leroy presenting the OCaml 4.02 release

Xavier Leroy presenting the OCaml 4.02 release

Today: What's new in OCaml 4.02 (September 2014)? Many new features; 66 bugs fixed, 18 feature wishes.

1. Unified matching on values and exceptions

Classic programming problem: in let x = a in b, we want to catch exceptions raised by a but not those raised by b. In OCaml 4.02, the match .. with .. construct is extended to analyse both the value of a and exceptions raised during a's evaluation.

let rec readfile ic accu =
  match input_line ic with
  | "" -> readfile ic accu
  | l -> readfile ic (l :: accu)
  | exception End_of_file -> List.rev accu

Compilation preserve tail-call optimization.

2. Module aliases

Common practice: bind an existing module to another name.

module A = struct
  module M = AnotherModule

This binding is traditionally treated like any other definition. This can cause subtle type errors with applicative functors; accesses sometimes go through more indirections; linking problems.

In OCaml 4.02, these constructs are treated specially during type-checking, compilation and linking.

Discussion of the application of this to libraries.

3. Mutability of strings

For historical reasons, the string type is mutable in place and has two broad uses. It can be used to represent text, or as an I/O buffer (mutable). OCaml programmers usually keep these two uses distinct. But mistakes happen, and user code may be malicious.

In the Lafosec study (ANSSI), there are a few examples of OCaml code where one can mutate someone else's string literals, or make a Hashtable key disappear.

New solution by Damien Doligez: in 4.02, there are two base types -- string for text, and bytes for byte arrays. By default, the two are synonymous but incompatible if option -safe-string is given.

In default mode, all existing code compiles but get warnings when using String functions that mutate in place.

In -safe-string mode, the values of type string are immutable (unless unsafe coercions are used); I/O code and imperative constructions of strings need to be rewritten to use type bytes and library functions from Bytes.

Other novelties: explicitly generative functors; annotations over OCaml terms; external preprocessers that rewrite the typed AST (-ppx option); open datatypes, extensible a posteriori with additional constructors.

Performance improvements: more aggressive constant propagation, dead code elimination, common subexpression elimination, pattern-matching, printf (based on GADTs); representation of exceptions without arguments

New toplevel directives to query the environment; new port to 64-bit ARM; source reorganization: Camlp4 and Labltk split off the core distribution and now live as independent projects.

What's next? Bug fix release 4.02.1, then various language experiments in progress; more optimizations, ephemerons, GDB support, tweaks to support OPAM better.

Q: Safe Haskell is starting to catch on in the Haskell community.
A: "Safe strings" are a first step in this direction. Safe OCaml is definitely something we're thinking about.

The OCaml Platform v1.0, by Anil Madhavapeddy (presenting, C), Amir Chaudhry (C), Jeremie Diminio (JS), Thomas Gazagnaire (C), Louis Gesbert (OCamlPro), Thomas Leonard (C), David Sheets (C), Mark Shinwell (JS), Leo White (C), Jeremy Yallop (C); (C = University of Cambridge, JS = Jane Street)

Anil Madhavapeddy presenting the OPAM package manager and platform

Anil Madhavapeddy presenting the OPAM package manager and platform

The platform: tooling, quantitative metrics, agility to judge the impact of language changes. Ultimate goal: grow a sustainable open source community.

OPAM 1.2: "The Platform Release" -- solver errors are explained in plain English rather than boolean formulae, more expressive queries (reverse dependencies and recursive); can clone the source code and repo file for any OPAM package.

Steady growth in the number of released OPAM packages. Growth in number of contributors is much slower.

New workflow in OPAM 1.2 (See -- flexible clone, pin, configuration, sharing.

OCaml Platform? OPAM Platform! Tools built around OPAM that provide a modular workflow for developing, publishing and maintaining OCaml source code, both online and offline.

OPAM = OCaml PlAtformM!

OPAM 1.2 restructured: everything built on OpamLib. On top of that library, have "opam", "opam-publish", "opam-doc" (with opam-units) tools.

OPAM documentation -- goal: documentation unified across packages that handles cross-referencing and module inclusion well. It's hard!

Use only the Typed AST; comments are transformed into attributes in the typed AST; these are used by external tools.

Preview: a working prototype.

Timeline: Sept online release, Nov use it locally, Dec build custom website for other repositories.

Tooling: OCamlJS now supports complete compiler REPL in JS; GDB integration.

Polish: easier to package and install; binary releases on lots of platforms; documentation rewritten.

One More Thing: Assemblage -- an very alpha eDSL to describe OCaml projects. OCaml as host language with Merlin auto-completion. Can introspect the project description to generate build rules. Very lightweight, integrates easily.

Q: Do you have anything to help with ARM cross-compilation?
A: Global build "glue" still required to support cross-compilation (Assemblage will help with this)

Q: Any platform is a (false?) promise to define a set of supported libraries. What's OPAM's take on this?
A: We as the developers of the platform don't want to be the people who define what the current set of packages considered as "stable" is. We just build the tools.

Session 4: Language

A Proposal for Non-Intrusive Namespaces in OCaml, by Pierrick Couderc (I), Fabrice Le Fessant (I+O), Benjamin Canou (O), Pierre Chambart (O); (I = INRIA, O = OCamlPro)

The namespace proposal presentation

The namespace proposal presentation

In OCaml, we cannot use multiple modules that have the same name.

Common practice: use long names, e.g. LibA (Misc, Map) as LibA (LibA_Misc, LibA_Map).

Packs: for the developer -- no code change, simply a matter of options; user -- use path to distinguish modules. Cons: too many recompilations, dependencies, large executables!

In 4.02, we can use module aliases. Option -no-alias-deps.

Advantages: can deceive ocamldep for better dependencies and namespaces can be used transparently.

Proposed solution: namespaces and "as" imports. Can import specific modules, or all modules, or all modules except certain ones from a namespace.

Namespaces are not closed -- adding a module a posteriori is possible.

Comparison with module aliases: +extensibility, +simple build system, +better dependencies, +expressivity. But -new syntax.

Work in progress: big functors -- using packs to generate functors.

Conclusion: mechanism of namespaces integrated in the language, solves compilation issues. Working prototype for 4.02:

C: Openness vs. functorization.
A: With big functors, one idea would be to close namespaces but that would not be ideal.

C: There are already module aliases and then there will be namespaces. Mixing the idea of big functors with namespaces does not seem like a good idea.

Q: How about qualified imports?
A: Interesting idea.

Q: Why do you need "in namespace" if you're already separating by directory structure?
A: It's not enough. We need to separate compilation units.
C: But directory names could be used a link time.

C: The benefits must be very clear because there already are lots of ways of importing "stuff".

Improving Type Error Messages in OCaml (*), by Arthur Charguéraud (INRIA & Université Paris Sud)

Arthur Charguéraud presenting his improved type error messages

Arthur Charguéraud presenting his improved type error messages

Type errors: dozens of papers, no implementation. For beginners and experts, type errors can be very frustrating.

Result: a patch to the type-checker, providing alternative error messages for ill-typed toplevel definitions.

Example: missing unit argument to read_int -- report "You probably forgot to provide '()' somewhere"; refs and bangs; missing "rec" -- report "You are probably missing the 'rec' keyword".

Example: missing else branch -- laughs from the audience about the terrible type error message.

Other common errors where the error messages were improved: subexpressions, ill-typed applications, binary operators, incompatible branches, higher-order function application, occurs check errors.

Also works for optional and named arguments.

Not included GADTs, module type checking.

Try it online:

Q: Why can't we have this today??
A: Need more testing, especially with higher order functions.

Github Pull Requests for OCaml development: a field report (*), by Gabriel Scherer (INRIA).

Experiment of using Github pull requests for 8 months.

Old way: Mantis.

users report bugs, request features, propose patches
sometimes a developer works on a PR
a release each year, with some bugs fixed and some new features

Serious bugs get fixed, features are mostly ignored. Patches got ignore too!

Results of the experiment: 18 new contributions (patches) from people who probably wouldn't have sent their patches in the old system, 18 new reviews! So Github attracted a new crowd of contributors.

Some Github pull requests were handled very quickly (small fixes and changes). Developer meetings help taking decisions. Pull requests are most effective during initial development: before the feature freeze (after that the team is busy with getting the release out).

Negative results: to be effective, external users should be told more about the development cycle. Github was used mostly by people used to git.

Q: Reviews and contributions?
A: 0 reviews on Mantis, 18 on Github; ~40 patches on Mantis, 18 on Github.

Q: How about switching to git?
A: Don't believe that is going to happen. There is so much information on Mantis right now! Continuous integration is nice though.

Joint Poster Session (with ML Family workshop)

Irminsule; a branch-consistent distributed library database, by Thomas Gazagnaire (C), Amir Chaudhry (C), Anil Madhavapeddy (C), Richard Mortier (University of Nottingham), David Scott (Citrix System), David Sheets (C), Gregory Tsipenyuk (C), Jon Crowcroft (C); (C = University of Cambridge)

What if you had a distributed database with git-like semantics? -- E.g. commit history

Problem: merges!

Have an Obj and a Git backend. Implementations of various persistent data structures with merge function: prefix trees, mergeable queues, mergeable ropes.

A Case for Multi-Switch Constraints in OPAM, by Fabrice Le Fessant (INRIA)

Fabrice Le Fessant making the case for multi-switch constraints in OPAM

Fabrice Le Fessant making the case for multi-switch constraints in OPAM

OPAM: the official way to install OCaml packages. Builds a CUDF universe with packages available for the switch, and the dependency constraints between these packages. Send the universe to the CUDF solver, then apply the solution to the switch.

Multi-switch constraints: add a switch prefix to each package name. allow dependency constraints between packages with different switch prefixes

Use cases: cross-compilation (build and host switches), multi-switch packages, all-switch commands, per-switch repositories, external dependencies, applications-specific switches.

No implementation yet -- just an idea for discussion.

LibreS3: design, challenges, and steps toward reusable libraries, by Edwin Török (Skylable Ltd.)Slides Poster.

Edwin Török presenting the LibreS3 library

Edwin Török presenting the LibreS3 library

Concepts: monads, S3 server -- proprietary Amazon service; FOSS replacements exist.

Architecture: don't choose one particular library/framework; be careful with acquiring resources: with_resource; some code in LibreS3 has been generalized (any-cache, any-http)

Found some interesting bugs in OCaml and libraries while developing LibreS3. Debugging monadic code is hard! Stack traces from monadic concurrency are incomplete.


Nullable Type Inference (#), by Michel Mauny and Benoit Vaugon (ENSTA-ParisTech)

Nullable Type Inference presentation

Nullable Type Inference presentation

Nullable type t? includes NULL and values of type t. Provide type inference algorithm featuring HM polymorphism that statically guarantees that NULL cannot be used as a regular value (algorithm's soundness has been proved).

Nullable types are used in practice: Hack (Facebook), Swift (Apple)


Coq of OCaml, by Guillaume Claret (Université Paris Diderot)

OCaml: FP with imperative features, many libraries and programs.

Coq: mainly used as a proof language, purely functional (only terminating programs!), dependent types, limited number of libraries.

Coq to OCaml: extraction mechanism developed by Pierre Letouzey -- removes proof terms, complete.

OCaml to Coq: CFML: deep embedding -- how to do shallow embedding? How to import imperative programs? How to keep the resulting code small?

Use a monadic translation to represent imperative features in Coq.

Usage: prove formal properties on OCaml programs, augment number of Coq libraries for programming.

Effects descriptor: a set of atomic effects. Inference: infer types using the OCaml compiler, then bottom-up analysis; fixpoint for mutually recursive definitions. Then represent effects as monads in Coq.

There is one monad per descriptor of effects -- how do we compose them? Impossible in general, but doable when we restrict the shape of the monads.

Monads exist to model global references, exceptions, i/o, non-termination.

Supported language fragment: pure lambda-calculus kernel, mutually recursive functions, inductive types, records, ADTs, ...

Compilation passes: 1. import the syntax tree typed by the OCaml compiler; 2. infer effects; 3. do monadic transformation; 4. pretty-print to Coq syntax.

Challenges: generate human-readable code, support real OCaml programs, import basic libraries, support functors (not currently implemented).

Conclusions: hard to make compiler work on real examples; functors are complex!

Q: The granularity of effects matters a lot when you use this to prove properties of your OCaml code.
A: Yes.

Q: How can I use this to prove properties about recursion -- e.g. whether they are tail-recursive?
A: (?)

Q: Do you handle local references?
A: Not at this moment.

Q: You only support part of the Pervasives library. Which parts do you not support?
A: Can't currently pass effectful functions as arguments, so for example List.iter is not supported in the usual use case.

High Performance Client-Side Web Programming with SPOC and Js of ocaml (*), by Mathias Bourgoin and Emmmanuel Chailloux (Université Pierre et Marie Curie)

Mathias Bourgoin presenting SPOC

Mathias Bourgoin presenting SPOC

This is the OCaml Users and Developers Workshop 2014 in Gothenburg.

Parallel OCaml? Lots of libraries. One is SPOC = Stream Processing OCaml (with OpenCL).

GPGPU programming. Two main frameworks: Cuda and OpenCL. Use different languages to write kernels (Assembly or C/C++ subset) and to manage kernels (more or less any general purpose language can be used here).

SPOC compiles to Cuda or OpenCL. It contains the Sarek DSL and a runtime.

WebSpoc: compile OCaml with SPOC and then to JS with js_of_ocaml.

Demo: Using SPOC from within a browser to do image manipulation.

WebSpoc helps develop complex web apps with intensive computations. Good for GPGPU courses.

Future work: add a JS memory manager dedicated to SPOC vectors; add bindings to WebGL.

Demo online:

SPOC and Sarek are available in OPAM.

Q: Is there potential to run this code on the GPU of a mobile phone?
A: Not yet.

Using Preferences to Tame your Package Manager, Roberto Di Cosmo (presenting, D+I), Pietro Abate (D), Stefano Zacchiroli (D), Fabrice Le Fessant (I), Louis Gesbert (OCamlPro); (D = Université Paris Diderot, I = INRIA)

Ten years of research on package management -- EDOS and MANCOOSI, bridging research communities. Used as foundation of OPAM.

LOTS of package managers out there! Binary, source, language specific, application specific, decentralized, functional approach.

What's inside? Two sides: people maintaining packages (server side) -- maintain a coherent set of packages. Also "client side" -- fetch and authenticate metadata and packages, resolve dependencies, user preferences.

Dependency solving: installability of a single package and co-installability of a set of packages are NP-complete problems.

Application: find uninstallable packages in a repository: just call a SAT solver on each package in the repository! ("dose" library is specialized for this task) is the OPAM Weather Service -- shows which packages can be installed together.

Finding a solution is NP-complete but installing and upgrading is more demanding -- how many ways are there to install a package? Exponentially many.

Towards modular package managers. 0. stop coding a petty solver for every new component base system; 1. use a common upgrade description format; 2. provide means for expressing user choice.

User preferences: built from four ingredients. Package selectors, measurements, maximisation/minimisation, aggregation.

Example for minimisation: -count(removed) -- we want a solution where the number of removed packages is minimised.

Example profiles:

"Paranoid": -count(removed),-count(changed)
"Trendy": -count(removed),-notuptodate(solution),-count(new)

Slightly more exotic:

"Minimal system": -sum(solution,installedsize),-count(solution)
"Noah's ark": +count(solution)
"Fast bootstrap": -sum(solution,compiletime)

Repairing a broken system configuration:

"Fixup simple": -count(changed)
"Fixup trendy": -count(changed),-count(down),-notuptodate(solution)

This is all possible with OPAM 1.2! (Check opam --help, look for --criteria)

You can help: try different profiles, test expressivity of the preference language, help debug OPAM.

Conclusion: package managers are complex; a very hard part is dependency solving! Modern package managers must share common components, in particular dependency solvers and the user preference language.

Q: Does this keep track of explicitly installed packages?
A: Yes, OPAM knows which packages were installed directly (the "root set") and which were installed in order to satisfy dependencies.

Q: Why don't other package managers use this technology now?
A: Package managers are core parts of any system so people tend to be very resilient to change.

Simple, efficient, sound-and-complete combinator parsing for all context-free grammars, using an oracle (*), by Tom Ridge (University of Leicester)

The P3 combinator parsing library. Can handle all context-free grammars (CFGs). Scannerless or can use a lexer. Good theoretical basis, but slow.

Example given.

Memoized counting -- you can't do this with any other parser!

Compute actions over all good parse trees; there are exponentially many such parse trees, but this doesn't have to take exponential time!

Disambiguation: directly encode in code with using "option".

Supports modular combination, e.g. "helper parsers".

Comparison with Happy: it's faster, in some cases ridiculously so.

Long term aim: general parser, verified correctness and performance, usable in the real world.

Q: What's the use case?
A: It's extremely flexible, but slower than deterministic parsers.

Filed under: Conference, Workshop 1 Comment

ICFP 2014: Day 3

Posted by hh360

Good Morning from the 3rd and final day of the 19th International Conference on Functional Programming, as ever Leo and I (Heidi) are here to bring you the highlights.

Keynote (Chair: Edwin Brady)

Depending on Types

Stephanie Weirich (University of Pennsylvania

Is GHC a dependently type PL ? Yes*

The story of dependently typed Haskell ! We are not claiming that every Agda program could be ported to Haskell

Example: Red-black trees (RBT) from Agda to haskell

All the code for today's talk is on github:

Example of insertion into RBT from Okasaki, 1993.

How do we know that insert preserves RBT invariants ?
We are going to use types

Examples RBT in Agda from Licata. We can now turn this into GHC using GADT's and datatype promotion.

Haskell distinguishes types from terms, Agda does not.

In Haskell types are erased at runtime unlike adge.

Datatype Promotion: Recent extension to GHC, we can't promote things like GADT's though.
GADT's: introduced to GHC 10 years ago, the really challenge was integration with Hindley-Milner type inference

Silly Type Families - GADT's from 20 years ago

Our current insert to RBT needs to temporary suspect the invariances for RBT, but how can we express this in the haskell type scheme ?

Singleton Types: Standard trick for languages with type-term distinctions.

The difference between Agda and haskell: Totality

What's next for GHC ?
Datatype promotion only works once

- TDD: Type-driven development (as described by norman yesterday)
- Totality checking in GHC
- Extended type inference
- Programmable error messages
- IDE support - automatic case splitting, code completion and synthesis

GHC programmers can use dependent type*

Q: Why not use incr in the definition to unify the two constructors ?
A: You can, I was just simplifying for explanation

Q: Its seems like this increase cost, is this the case ?
A: Yes, but not too much

Q: Are dependent types infectious ?
A: Good questions, no we can add a SET interface to this RBT to abstract

Q: To what extend is Haskell the right place for these new features ?
A: Its a great testbed for industrial scale and we can take lessons back into dependently typed

Q: Can you do this without type families ?
A: Yes, I wanted show type families

Q: Why not just use Agda ?
A: Haskell has large user base, significant ecosystems, 2 decades of compiler dev

Q: What's the story for keeping our type inference predictable ?

Q: What level of students could use these ideas ?
A: I've used in advanced programming for seniors

Session 10: Homotopy Type Theory (Chair: Derek Dreyer)

Homotopical Patch Theory

Carlo Angiuli (Carnegie Mellon University); Ed Morehouse (Carnegie Mellon University); Daniel Licata (Wesleyan University); Robert Harper (Carnegie Mellon University)

Idea: define a patch theory within Homotopy Type Theory (HoTT)

Functorial semantics: define equational theories as functors out of some category. A group is a product-preserving functor G -> Set

Homotopy Type Theory: constructive, proof-relevant theory of equality inside dependent type theory. Equality proofs a = b are identifications of a with b

Can have identifications of identifications.

Higher Inductive Types introduce non-sets: arbitrary spaces.

Patch Theory defines repositories and changes in the abstract -- model repositories, changes, patch laws governing them.

Key idea: think of patches as identifications!

Bijections between sets X and Y yield identifications X = Y.

Example: repository is an integer; allow adding one. Everything works out nicely.

Next example: repository is a natural number; allow adding one. Problem: can't always subtract one (which would be the inverse of adding one)! -- solved using singleton types.

Closing thoughts: computation vs. homotopy. There is a tension between equating terms by identifications and distinguishing them by computations. Analogy: function extensionality already equates bubble sort and quicksort -- they are the same function but different programs. Computation is finer-grained than equality.

C: Very understandable talk! But I still need to see a talk / paper where Homotopy Type Theory guides me to something new.
A: The functorial part comes for free within HoTT. Also we're forced to do everything functorially.

Q: Patch computation -- does your theory say anything about that?
A: Didn't work out nicely.

Q: Can you give me some intuition about the singleton type used in the naturals example?
A: This is a hack. We want a directed type that does not have symmetry.

Q: What about higher layers?
A: Don't need to go further than the second layer when dealing with Sets.

Pattern Matching without K

Jesper Cockx (KU Leuven); Dominique Devriese (KU Leuven); Frank Piessens (KU Leuven)

Pattern matching without K

Dependent pattern matching is very powerful, but HoTT is incompatible with dependent pattern matching. The reason: it depends on the K axiom (uniqueness of identity proofs), which HoTT does not allow. But not all forms of dependent pattern matching depend on the K axiom!

Dependent pattern matching. Have a dependent family of types -- example of pattern matching given with dependent pattern matching and without.

K is incompatible with univalence.

How to avoid K: don't allow deleting reflexive equations, and when applying injectivity on an equation c s = c t of type D u, the indices u should be self-unifiable.

Proof-relevant unification: eliminating dependent pattern matching. 1. basic case analysis -- translate each case split to an eliminator; 2. specialization by unification -- solve the equations on the indices; 3. structural recursion -- fill in the recursive calls.

Instead of McBride's heterogeneous equality, use homogeneous telescopic equality.

Possible extensions: detecting types that satisfy K (i.e. sets -- allow deletion of "obvious" equations like 2 = 2); implementing the translation to eliminators (this is being worked on or is done but not yet released); extending pattern matching to higher inductive types.

Conclusion: by restricting the unification algorithm, we can make sure that K is never used -- no longer have to worry when using pattern matching for HoTT!

Q: What was your experience contributing to Agda?
A: Not a huge change -- not too hard.

Q: Is there a different way of fixing the "true = false" proof?
A: K infects the entire theory.

Q: You said the criterion is conservative. Have you tried recompiling Agda libraries? What did you observe?
A: 36 errors in the standard library, 16 of which are easily fixable, 20 are unfixable or it is unknown how to fix them.

Q: Is K infectious?
A: Yes -- when activating the "without K" option, you cannot use a library that defines something equivalent to K.

Session 11: Abstract Interpretation (Chair: Patricia Johann)

Refinement Types For Haskell

Niki Vazou (UC San Diego); Eric L. Seidel (UC San Diego); Ranjit Jhala (UC San Diego); Dimitrios Vytiniotis (Microsoft Research); Simon Peyton-Jones (Microsoft Research)

Refinement Types: types with a predicate from a logical sub-language

can be used to give functions preconditions

Example: division by zero.

ML, F#, F* have had refinement type implementations but they're CBV! How can this be translated to Haskell?

CBV-style typing is unsound under call-by-name evaluation.

Encode subtyping as logical verification condition (VC)

Under call-by-name, binders may not be values! How to encode "x has a value" in CBN? Introduce labels to types (giving stratified types): label values that provably reduce to a value.

How to enforce stratification? -- Termination analysis! Can use refinement types for this.

Can check non-terminating code. (collatz example)

How well does it work in practice? LiquidHaskell is an implementation of a refinement type checker for Haskell.

Evaluation: most Haskell functions can be proved to be terminating automatically.

Q: What's a stratified algebraic data type?
A: It's an ADT with a label.

Q: How do you prove termination of structurally recursive functions?
A: Map to numbers (e.g. list to length)

Q: Refinement types for call-by-push-value?
A: There is some work on refinement types which does not depend on the evaluation strategy.

Q: In the vector algorithm algorithm library, every function had to be annotated.
A: Here the first argument usually increases rather than decreasing so had to write a custom metric but this was simple.

Q: How does this interact with other features of Haskell like GADTs?
A: LiquidHaskell works on Core so there should be no problem.

Q: Did you find any bugs in your termination analysis of different libraries?
A: Yes a tiny bug in Text, has been fixed now.

A Theory of Gradual Effect Systems

Felipe Bañados Schwerter (University of Chile); Ronald Garcia (University of British Columbia); Éric Tanter (University of Chile)

Programs produce results and side-effects. Can use type and effect systems to control this. Here, functions have an effect annotation -- a set of effects that the function may cause. Expressions are also annotated with their effects.

Get into trouble when passing an effectful function to another function: as a safe approximation, the higher-order function must always assume that the function passed in may have any possible effect.

Introduce "unknown" effect (upside-down question mark). Combine this with a generic type-and-effect system which comes with "adjust" and "check" funtions.

Use abstract interpretation to lift adjust and check from static to gradual typing. Idea: extend the domains of sets to include "unknown" side-effects.

Define concretization function from consistent privileges to the feasible set of effect sets.

Introduce internal language: with "has" and "restrict" constructs.

Future work: implementation (currently targeting Scala); Blame

Q: Can you always derive the concretization function?
A: It is provided by us.

Q: If you're doing dynamic checking and dealing with arrays, but you can't raise errors -- what are you supposed to do?
A: You don't want to produce a side effect when you're not allowed to do so.

Q: Does your system allow parameterization on effect sets?
A: No, not in this paper. The implementation requires it though so this is future work.

Session 12: Dependent Types (Chair: Ulf Norell)

How to Keep Your Neighbours in Order

Conor McBride (University of Strathclyde)

Conclusion first: Push types in to type less code!

A very engaging and amusing talk. Conor demonstrates (with lots of Agda code!) how to use types to build correct binary heaps and 2-3 trees.

Take-aways: Write types which push requirements in. Write pograms which generate evidence.

Q: What does "push requirements in" mean?
A: Start from the requirements. The information that's driving the maintenance of the requirements is passed in. (?)

A Relational Framework for Higher-Order Shape Analysis

Gowtham Kaki (Purdue); Suresh Jagannathan (Purdue)

Shape analysis in functional languages: ADTs track shapes of data. Types can document shape invariants of functions -- but we want to be able to refined these constraints!

Can we refine ML types to express and automatically verify rich relationships? Need a common language to express fine-grained shapes. Build up from relations and relational algebra. Example: in-order and forward-order.

Relational operators are union and cross
Predicates are equality and inclusion

But: relational types for polymorphic and higher-order functions must be general enough to relate different shapes at different call sites. Examples: id, treefoldl : a tree -> b -> (b -> a -> b) -> b

Consider id : a-> a -- shape of argument is the shape of its result.

Functions can be parameterized over relations. Also get parametric relations: relations can be parameterized over relations.

We consider the "effectively propositional fragment" of many-sorted first-order logic. Key observation for translation: a fully instantiated parametric relation can be defined in terms of its component non-parametric relations.

Implementation: CATALYST,

Q: Does your type system allow the user to introduce new relations?
A: Yes.

Q: Can you compile any kind of parametric relation or are there restrictions?
A: You can also write relations on infinite data structures.

Session 13: Domain Specific Languages II (Chair: Yaron Minsky)

There is no Fork: an Abstraction for Efficient, Concurrent, and Concise Data Access

Simon Marlow (Facebook); Louis Brandy (Facebook); Jonathan Coens (Facebook); Jon Purdy (Facebook)

Imagine you a web program, the server side code for generating a webpage, with many sources. For efficient, we need concurrency, batching, caching. We could brute force, but that destroys modularity and we its messy.

What about using explicitly concurrency ? It really on the programmer too much e.g. they forget to fork, add false dependancies.

A rule engine: input, evaluate rules and return.

Consider coding a blog, example code including Fetch monad. The concurrency is implicit in the code, just use monand and applicative.

No manual batching, again implicit in code.

Demonstrating code for map and applicative. The monadic bind gives a tree of dependencies for IO.

This was developed for the haxl project at FB, migrating from an in-house DSL called FXL to Haskell. The code framework is open-source, and cabal install haxi.

Implicit parallelism is hard but data fetching doesn't suffer from this problem.

Q: Is binder linear in depth of blocked requests ?
A: Take it offline

Q: Is there accidental sequential code ?
A: We haven't rolled it out yet

Folding Domain-Specific Languages: Deep and Shallow Embeddings (Functional Pearl)

Jeremy Gibbons (University of Oxford); Nicolas Wu (University of Oxford)

Introducing a trivial toy language for examples. Defined a simple deep and shallow embedding for the toy language. Shallow embedding must be compositional. The toy language have eval and print, print is messy to add to shalow embedding.

Dependent interpretations is introduced for the deep embedding and again is ok to shallowing embedding but not great. Context senstive interpretation can again be expressed as a fold.

Deep embedding are ADT's, shallow embeddings are folds over that AS.

Q: Has you method for shallow embedding sacrificed extensibility ?
A: Yes but we can work around it

Q: What about binders, what about recursion ? You example is too simple.
A: Not sure

Session 14: Abstract Machines (Chair: David Van Horn)

Krivine Nets

Olle Fredriksson (University of Birmingham); Dan Ghica (University of Birmingham)

We're good at writing machine-independent good. But how about architecture independence? (CPU, FPGA, distributed system).

Work: underlying exeution mechanism for suporting node annotations t @ A.

Approach: put your conventional abstract machines in the cloud.

Krivine net = instantiation of a network model with Distributed Krivine machine. Network model: synchronous message passing and asynchronous message passing.

Basic idea of execution: remote stack extension.

Krivine nets perform much better than previous approaches (GOI and GAMC).

Future: work on runtime system -- failure detection and recovery, may need GC.

Implementation and formalization:

Q: How about more complex data like arrays which are expensive to send over the network?
A: We're working on this -- introduce a notion of mobile and immobile.

Distilling Abstract Machines

Beniamino Accattoli (University of Bologna); Pablo Barenbaum (University of Buenos Aires); Damiano Mazza (Université Paris 13)

Setting: lambda-calculus. There has been a lot of research into the relationship between abstract machines and substitution. We introduce the Linear Substitution Calculus which decomposes beta-reduction. It is equipped with a notion of structural equivalence.

Origins: proof nets: a graphical language for linear logic. Multiplicative cut (symmetric monoidal closed category), exponential cut (for every A, !A is a commutative comonoid)

Introduce Linear Substitution Calculus -- proof nets in disguise. Have multiplicative and exponential step.

Then define reduction strategies on it: by name / by value (LR / RL) / by need require a specific form of evaluation context together with specific versions of the multiplicative and the exponential step.

Distilleries: choice of deterministic multiplicative and exponential evaluation rule and an equivalence relation which is a strong bisimulation; an abstract machine with transitions defined by the chosen rules; a decoding function. Moral: the LSC is exactly the abstract machnie "forgetting" the commutative transitions. In all cases, distillation preserves complexity.

Q: What does the fourth possible combination of multiplicative and exponential evaluation correspond to (both by value)?
A: That would be a sort of call-by-value, depending on how you define the context.

Q: Can you bring some order to the seemingly unrelated evaluation contexts?
A: With these contexts, you tell the machine where to look for the next redex. You can even do non-deterministic machines. At some point, your choice is limited if you want execution to be deterministic.

Q: Are these evaluation contexts maximally liberal while staying deterministic?
A: Yes.

Q: From reduction semantics to an abstract machine is a mechanical process. Is it possible to find an algorithm that generates distilleries?
A: I can do it by hand ... don't know if there is an algorithm.

Student Research Competition Award Presentation

14 submissions; 13 accepted (7 grad, 6 undergrad); 3 winners for each category.

ICFP 2015 Advert & Closing

ICFP 2015 will take place in Vancouver, Canada:

The venue is great -- connectivity, location etc.

New: will have a Mentoring Workshop for beginning graduate students doing research. Workshop proposals and paper submission deadlines are on the website.


ICFP 2014: Day 2

Posted by Leonhard Markert

This is day 2 of the 19th International Conference on Functional Programming. Leo and I(Heidi) are here bright and early to bring you the highlights from Gothenburg, Sweden. We are not the only livebloggers here at ICFP, check out Edward Yang's excellent notes.

Keynote (Chair: Jesse Tov)

Behavioral Software Contracts
Speaker: Robert Bruce Findler (Northwestern University)

Intro: Robby's work on behavioural software coIMG_0008ntracts is beautiful :)

Instead of an abstract, the keynote has a bibliography of interesting work in the area

Contracts are infectious

Example of racket code, working in a call-by-value language here a point-in function. Demonstration of contract violation including a blame, from applying bools instead of integers. Robby's adds an contract to guard access to point-in and the blame now points to the top-level.

Graph demonstrating how the number contracts in Racket in git has constantly increased. Robby identifiers the outliers e.g. big libraries with lots of contracts have been introduced. Most commits add 1 contract, if they add any.

Everyone is the audience is thinking, why use contracts when you can use types, as identified by Robby.

Contracts Verse Types: The previous example is used with types instead of contracts and negative integers are used to demonstrate how types are insufficient. He introduces a dependant contract, hinting at a comparison to dependent types. We have the arguments at the time of checking unlike dependant types hence its actually possible in real languages.

Switch to an example of a function called dc, which takes a drawing function and bonding box and draws as a side effect. Robby introduces a broken function, which introduces state pollution. We can't check that state after equals the state before, but this would be too late. Here's a contract example, generating a random state and testing for state pollution, this doesn't give us complete corrects but its good enough in this contexts.

Don't think about contracts in terms of projections, but in terms of boundaries (work by Christos)

Addition of contacts to simply typed lambda calculus, in particular how monitors add contracts to boundaries. Example of if we have contract (x =< 3), how values of x > 3 causes the function to abort. If we have another contract to checks f's arguments and return values are x=<3, we allow the function to passed though to with extra monitors on return values.

What happens if we have a bug in the monitor ?

Applications: How can contracts work if you don't give have access to the code the other side of the boundary. We can use the contract exercise function to generate random functions to test monitors. We have an notion of whether one contract is stronger than another.

Gradual typing: How we interfere between typed and untyped languages, see how we do it with Typed Racket.
Static Analysis: Havic, exhaustive boundary checking

This presentation is actually written in racket, all output is real output.

Takeaways: Contracts are not just crappy types, contracts infect code bases, boundaries are vital.

ICFP knows how to prove specifications. Contract infectiousness is our opportunity to share that knowledge with the world

Q: Randomisation would break formal tools, why not use NaN ?
A: Just setting to default may not find errors

Q: Contracts are checked at runtime, what about the overhead ? Is there a switch to turn it off ?
A: There's no kill switch, but there are option contracts

Q: Effects are alarming !
A: Well, it doesn't alarm me, its vital for practical code and we need to look into this further

Q: Why can't we introduce randomness to type checking ... ?
A: that the point

Q: Thank you for the cross community impact !

Q: How is the blaming system implemented ? Can we add it to python or C ?
A: Fundamentally we could do this, we would love to see more of this.

Q: The opposite of gradual typing, often checks aren't just typed based. Can we use types to check for placement of contracts ?
A: Contracts are specification, they can be wrong like normal programs.

Q: Dependent contracts seems to explicitly pass all arguments, its looks a bit strange ?
A: Explicitly labelling dependency makes it cheaper



Session 6: Contracts & Scheme (Chair: Michael Sperber)

Soft Contract Verification
Phuc C. Nguyen (University of Maryland); Sam Tobin-Hochstadt (Indiana University); David Van Horn (University of Maryland)

This is the snake game with contracts, most computation time is executing contracts not code, there is serious overhead.

Contract and blame avoidance cycle, how can we then introduce static contract verification.

We will consider soft contract verification:
- we can analysis first class contracts
- using SMT solver
- competitive
- works even without apparent contracts


Introduce PCF language, and introducing contract notation.

How can we verify CPCF ? We abstract CPCF function to a type, symbolic values are sets of contracts.
Soundness: All concentration are approximated by CPCF

Check refine values are they flow through, these refines can influence the computation.

Theorem: Verified modules can't be blamed - Proved
We proof well to soft typing, occurrence typing, H.O recursion schemes and dependent refinements and video games for the beginning.

Contracts fail at first order, only need a F.O solver for verification
Q: If we replace with pos? with >4, where you short cutting the SMT solver ?
A: Yes, the name was just a shortcut

Q: How to do you handle recursion ?
A: This is described in the paper

On Teaching How to Design Programs: Observations from a Newcomer
Norman Ramsey (Tufts University)

This is course in problem solving, not functional programming

1) Describe data, input, making data examples
2) Describing functions, making function examples
3) Creating code templates from types, fill in the template
4) Test, review and re-factor

Example of 2D-tree data definition and writing a function to find the nearest point.

What's hard of students ?IMG_0011

- Type directed development is difficult even though there's mechanical rules to apply
- "Purpose statements" are hard, student just ignore them and read code, this breaks down then they get to recursion

The Tech:
- You don't need 5 language levels
- Don't be fooled by the DrRacket IDE - designed for fill racket, but every time you compile, untested code is thrown in your face
- Go deep intro "world programs" - interactive apps by composing pure functions

Open Problem - How can we evaluate students code ?
- Is this code the produce of systematic design ?
- Experienced instructions use point deducts

HtDP: low cost, high reward

Q: Design recipes suggest how to evaluate student assignments, this is the solution to your problem ?
A: We'll sit down and talk about it



Session 7: Tools Used in Anger (Chair: Jacques Garrigue)

SML# in Industry: A Practical ERP System Development (Experience Report)
Atsushi Ohori (Tohoku University); Katsuhiro Ueno (Tohoku University); Kazunori Hoshi (NEC Software Tohoku, Ltd.); Shinji Nozaki (NEC Software Tohoku, Ltd.); Takashi Sato (NEC Software Tohoku, Ltd.); Tasuku Makabe (NEC Software Tohoku, Ltd.); Yuki Ito (NEC Software Tohoku, Ltd.)

Experience report from using SML# in production systems

With SML# with can produce robust systems, regoros specification and high quality flexible code. We conducted a pilot for using SML# for indeustry software, 15 month project with to man-months

Features of SML# for practical software development:
Normal ML features plus record polymorphism, direct C IMG_0012interface, seamless SQL integration and fully concurrent non-moving GC => mulit-core is simple

The system represents a typical business application.

Lessons learned: We had various issues with ML as a production language and its essential to select best of bread components.

To deploy ML with need to train people, software quality control systems.

Record polymorphism is very useful for data intensive application.

Q: What's the evidence that its reliable/productivity ? So no quantifiable evidence ?
A: We need to work on this

Q: Is accounting interesting ?
A: Yes, I enjoyed it

Lem: Reusable Engineering of Real-World Semantics
Dominic P. Mulligan (University of Cambridge); Scott Owens (University of Kent); Kathryn E. Gray (University of Cambridge); Tom Ridge (University of Leicester); Peter Sewell (University of Cambridge)

Sorry for the bombastic title

CS community has build many models of real world artifices, build by engineers instead of computer scientists. E.g. memory models, processor architectures etc.. This take many postdoc years to build.

Models developed inside theorem prover or PL and then hand ported, porting maybe machine assisted (perl scripts :)). This language to language translation is ugly. We have theorem prover lock-in. Porting requires experiments in both systems. There it not a library of off the shelf components.

Example of C++11 relaxed memory models

Can we have a write once, prove anywhere model ?

Example model: Power and ARM operational model of relaxed memory (Sarkar et al.)
Build from multiprocessor memory subsystem and thread semantics
Another Example model: Axiomatic model for C/C++11 concurrency
Another Example model: CakeML language semantics & verified compiler (Kumar et al.)

Introducing Lem:
Source -> Lem -> HOL, OCaml, Documentation
Lem syntax is similar to OCaml
Lem is a pragmatic tool, we are happy to accept code that causes problems for TP's
Example Lem including highlighting how whitspace and comments are presented

get lem from

Q: How to you support naming and binding ?
A: We would like to add nominal naming


Session 8: Type Systems (Chair: Geoffrey Mainland)

Safe Zero-Cost Coercions for Haskell
Joachim Breitner (Karlsruhe Institute of Technology); Richard A. Eisenberg (presenting, University of Pennsylvania); Simon Peyton Jones (Microsoft Research); Stephanie Weirich (University of Pennsylvania)

Generative type abstractions like newtype HTML = MkH String are useful to prevent programmer errors. The constructor has no runtime overhead. But stringList :: [HTML] -> [String] requires a "map", taking linear time!

Answer: a new equivalence relation, ≈ (written in Haskell syntax as Coercible a b instead of a ≈ b): coerce :: Coercible a b => a -> b which should do nothing at runtime!

Want: Coercible a b then Coercible [a] [b]

Need instances of Coercible for newtype ("wrapping") and data ("lifting") declarations.

Naive approach is too permissive: can derive Int ≈ Bool -> Bool when type families are used. Reason: have two equalities, nominal (compile time) and representational (run time) equality. Type families do not respect representational equality.

Answer: assign roles to type parameters -- nominal, representational, or phantom. Example for phantom: instance Proxy p1 ≈ Proxy p2

Role Inference. Goal is to determine the most permissive yet safe role for type parameters.

Application: GeneralizedNewtypeDeriving has been safely reimplemented in terms of coerce. Still only want to allow newtype (un)wrapping when constructor is in scope.

Abstraction: when do we want to allow coercion? The Default Debate: "preserve abstraction! Make roles default to nominal!" vs. "Be backward compatible! Allow GeneralizedNewtypeDeriving!" (latter was chosen).

Trouble on the horizon? Problem with Monads including "join".

Conclusion: efficient, safe, straightforward interface, implemented and released in GHC 7.8.

Q: Miranda dealt with ADTs rather differently. It later turned out that the Haskell and Miranda approach can be translated into each other.
A: Miranda's ADT system might run into trouble with type families...

Q: How many packages would need role annotations?
A: Not clear how to assess that. Probably very few (2?).

Q: How about Safe Haskell?
A: Coercible is not yet in the safe subset.

Q: Why not put these role annotations in the kind syntax?
A: Too big a change to the kind system. Makes it less flexible.

Hindley-Milner Elaboration in Applicative Style (Functional Pearl)
Francois Pottier (INRIA)

The story of ML type inference and how it has been explained over the years.

70s: Milner invents ML type system and polymorphism. Publishes declarative presentation, Algorithm W, and imperative one, Algorithm J. Contains global variable for "current substitution". Both compose substitutions produced by unification, and create new variables as needed.

80s: Cardelli, Wand formulate type inference as a two-stage process: generating and solving a conjunction of equations. Benefits: higher-level thinking: equations and conjunction instead of substitutions and composition; also greater modularity: constraint solving in library, generation by user. But new variables still created via global side effects.

90s: Kirchner and Jouannaud, Rémy explain new variables as existential quantification and constraint solving as rewriting -- necessary step on the road towards explaining polymorphic inference.

2000s: Gustavsson and Svenningsson explain polymorphic type inference using constraint satisfaction.

Constraint solving: on paper, every constraint can be rewritten until it is either solved or you get an error.

A problem: submitting a closed ML term to the generator yields a closed constraint which the solver rewrites to false or true (is well typed or is not well typed).

Question: can one perform elaboration without compromising the modularity and elegance of the constraint-based approach?

A "low level" solution: the generator could produce a pair of a constraint and a template for an elaborated term, sharing mutable placeholders for evidence so that after the constraint is solved, the template can be "solidified" into an elaborated term.

This approach has three stages: generation, solving, solidification; each user construct is dealt with twice -- not very elegant.

Can we describe generation and solidification in a unified manner? Yes: give the user a DSL to express computations which generate constraints and read their solutions simultaneously. The implementation of this is simple, and allows the user to define inference and elaboration in one inductive function.

Conclusion: solution is simple, modular, elegant, performant. Could be used in other settings potentially -- e.g. higher-order pattern unification?

Q: Would this work with lazy evaluation?
A: Not sure.

C: Notation of the algorithm is awkward.
A: Yes, this shows that we need a nicer way to express computations with applicative functors.

Q: How about a constructive proof instead of returning true or false?
A: Possible but then the proof tree would have to be inspected -- would still need to do work twice.

Session 9: Incremental Computing (Chair: Tiark Rompf)

Settable and Non-Interfering Signal Functions for FRP
Daniel Winograd-Cort (presenting, Paul Hudak (Yale University)

FRP: programming with continuous values and streams of events, like signal processing diagrams.

Used in Yampa, Nettle, Euterpea.

Event-based vs. continuous: introduce notation for differentiating the two from each other. Standard arrow operators: partial application, feedback loops, composition; stateful arrows (delay), arrow choice -- running the signal function becomes a dynamic decision.

Higher order arrows (with switch) are inherently dynamic, can replace arrow function. Arrows with switch are equivalent to Monad. Makes it harder to optimize.

Why arrows with switch? -- "power start and stop".

Contribution: introduce resettability and non-interfering choice. Get general resettability and arrowized recursion.

Example: IntegralReset. Want to be able to reset it with an event. Easy to do with switch: just replace the integral function. Without switch we can simulate a reset but we can't modify the function itself. Not nice: doesn't generalize.

Resetting state: state is in delays and loops.

General resettability: can take any signal function and transform it into a settable signal function.

Presents "settable laws": identity, uniformity, default.

Other example: pausing an arrow. IntegralWhen, a signal function that performs an integral only under a given condition. Can't do this with arrow choice. Solution: use non-interfering choice.

Non-interfering choice gives recursion! Two kinds: fixpoint-like recursion and structural recursion (which is static).

Arrowized recursion is "predictably dynamic" which nicely allows optimization.

Causal Commutative Arrows (CCA) can be heavily optimized. Allows choice but not switch. Reduces arrows to one of two forms.

Summary: introduced settability, a new model for controlling FRP state; non-interfering choice allows new forms of expression and arrowized recursion. Switch is really only needed for true higher order expressions!

Q: Is settability like a transistor?
A: Yes.

Q: Are there other circuit design ideas that we could re-use?
A: Not sure. Interesting idea.

Q: Can you use CCA factorization with GHC e.g. using rewrite rules?
A: Unresolved so far... There is a Template Haskell library for doing something like this.

Functional Programming for Dynamic and Large Data with Self-Adjusting Computation
Yan Chen (Max Planck Institute for Software Systems); Umut Acar (Carnegie Mellon University); Kanat Tangwongsan (Mahidol University)

Context: Big Data. Challenges are variety, volume, velocity -> requirements: expressive, parallel, incremental.

Why not use FP for computing big data? Parallelism can be done. How about incremental data?

Implicit self-adjusting computation: in addition to normal output, the program creates a dependency graph so when an input changes, only the necessary recomputation is done.

Result: faster but needs more memory to keep dependency graph -- time vs. space trade-off. This paper: controlling memory use.

Speedup as a function of memory usage has diminishing returns. Want to find sweetspot on the speedup vs. memory usage curve.

Type-directed translation: extend SML types with Changeable / Stable.

Need a technique to control granularity of the dependency graph. Treat blocks of data as a single reference cell. Fixed block size leads to disproportionately slow update time -- so instead use probabilistic chunking scheme.

Applications and Evaluation: incremental PageRank, graph connectivity, social circles. Very high speedups achieved in prototype implementations.

Future work: parallel self-adjusting computation.

Q: Have you done experiments with larger changes?
A: Yes.

Q: Is it realistic for companies to use 10x more memory in order to speed up computations?
A: It's a trade-off.

Q: What would be the speedup when you don't use additional memory?
A: Zero (?)

Q: This looks like it's intrinsically sequential. How do you plan to parallelize this?
A: It's all about dependency tracking -- dependencies can be used to decide where to parallelize.

Q: How would a non-pure merge sort perform?
A: Need to check.

Q: Can you incrementalize the whole or parts of SML?
A: Interesting question. Non-pure parts of SML would be problematic.

ICFP Contest Presentation by Duncan Coutts and Nicolas Wu

Created by members of the Department of Computer Science at Oxford University and Well-Typed.

The ICFP Programming Contest started in 1998 (in response to ACM programming contest allowing only mainstream imperative programming languages).

Open to anyone, any PL, teams of any size, takes place on the internet, 24h lightning round, 3-day full round.

Certain similarities between the problem ten years ago and this year's. But this year: two compilers instead of only one!

Setting the scene for this year's contest: 80s pop culture, 80s PL technology, 70s PL research.

LambdaMan runs around in a maze eating pills and evading ghosts ... striking resemblance to a more well-known game!

LamCo (producer of LambdaMan) had some interesting but weird technology and a spectacularly dysfunctional development process: team of 8-bit microcontroller fanatics and team of LISP fans combined.

Lightning round: LambdaMan AI running on LISP cpu
Full round: ghost AI running on 8-bit microcontroller in addition

Task description: many pages of cpu specs.

Simulator and debugger given -- in the "lingua franca of the internets", JavaScript (actually written in Haskell and the compiled using GHCJS).

Before the contest, pointless hints were tweeted.

Major panic as the git-based site deployment system went haywire just when the contest started, resolved manually. Next 5 hours spent with all hands on deck. Participants could see specification diffs on GitHub.

Judging process: collected and verified all submissions. Had enough resources to run all vs all rather than multiple-round tournament (one team's lambda man against another team's ghosts and the other way round). Ran 200220 games.

First prize: Supermassive Black Hom-set (Pavel Lepin) -- Haskell is the PL of choice for discriminating hackers!

Second prize: UnaGi -- C++ is a fine tool for many applications.

Third prize: DiamondPrincess -- Perl is also not too shabby.

Winner lightning round: (Alexey Shchepin) -- OCaml is very suitable for rapid prototyping.

Judge's prize: gagallium -- Team gagallium are an extremely cool bunch of hackers! Used OCaml. Best ghost. Used some intermediate stage of the OCaml compiler to compile their code from.

Pavel Levin gives a short presentation of his approach to solving the contest.

ICFP 2004 Most Influential Paper Award presented by Derek Dreyer

Goes to "Scrap More Boilerplate": Reflections, Zips, and Generalised Casts" by Ralf Lämmel and Simon Peyton Jones.

Significantly extended the authors' earlier "Scrap Your Boilerplate" paper and library.

Simon's observations: 1. an unequal partnership made this possible 2. good titles are powerful!

Ralf: used inspiration from different fields.


ICFP 2014: Day 1 (another perspective)

Posted by hh360

This is day 1 of the 19th International Conference on Functional Programming. Leo and I (Heidi) are here live blogging from Gothenburg, Sweden. He posts is here, we will combine them (with photos :)) in the coming weeks.


the beautiful conference location


Good morning and time to get started. Official definition of ICPF: its a forum for discussion of researchers. 466 people have registered for the conference or associated events. 21 sponsors giving $60K of sponsorship.

We can get a boat to the conference banquet :)

A big big thank you to the PC.


Keynote: Using Formal Methods to Enable More Secure Vehicles: DARPA's HACMS Program
Author & Speaker: Kathleen Fisher (Tufts University)

DAPRA, US funding organisation with rotating project management so constant surprises. Clean slate formal methods for proving security for embedded systems. Power, TV, medical devices, phones and vehicles, all have remote security risks with amusing exploits. Car is a computer on wheels, with many ECU's contacted with buses both high and low speed. If you can connect to the network, you can control anything under software control, getting to the network is easier then you'd think e,g using the diagnostic plug under the steering wheel. Almost all the car systems are under software control, include breaks. You can get access remotely by 4 ways: e.g. via bluetooth, allstar, virus to mechanises diagnostic equipment, via music to buffer overfull car software.

The traditional fix: air gaps & obscurity. Air gaps a no longer the case, cars need to be networked e.g. for remote monitoring.
Lots of documentation for this systems nowadays

The desktop approach fix: this approach barely works on desktop. The security issues with security code, approach 1/3 in recent watch list.
Current solutions are millions of LOC, extensive monitoring need extra CPU. Embedded codes goes though lots of review, no "patch every Tuesday" approach.

The new fix: Formal method based approach. Why now ? Lots of tools & infrastructure which is now mature and advances in SAT solves in later 10 years.

3 approaches:

code synthesis: people can't write secure C code, computers can and provide proofs. DSLs, Interactive theorem prover as PL.
HACMS program: collection of performers, bid for technical areas such as:
- Vehicle experts
- OS
- Control systems e.g. fly control
- Research Integration: managing rest of performers, composition, tools
- Red team: white hat hackers, measure effort needed to hack

Air team
- Boeing - unmanned little bird helicopter & open source version
- Galois
- RC*/U.Minn

All cars are vulnerable, no point in naming names.

composition means how do you get high assurance components and build a high assurance systems ?
Started with Pem testing 4 vehicles, 2 prep and 2 open source. The systems had been engineered to be easy to connect so this wasn't a surprise. Hijacking demonstration of AI drown using aircrack. This can be quite a shock to the user. e.g care driver. Ever 18 months, the red teams will report on how much effort is required to hack. Starting with monolithic quad. They began by upgrading processor and refactor monolithic software, the H/W abstraction layer was a big hit with the community. Gradually swapping out legacy code to high assure code.

How should you do if you know your under a DoS attack ? it depends on context

Its been proved that system is memory safe, ignore malformed messages, ignores non-authenticated and all good messages will reach motor control.

"The most secure UAV on the planet" :)

AADL - language for architecture specification used to propagate system wide properties down until an assurance it reached. A formal systematics haves been added to AADL and provided using resolute tool, assurance cases and synthesis tools. The glue code can be generated, this is typically the weak point.

Galois haven written two DSL's in Haskell, They have created Ivory for C like code and Tower for tasks/connections.

NICTA have written a fully functionally code micro kernel.

Boeing are using the tools build to develop the unmanned little bird. There on track for running real HACKEMS code on a helicopters in 2 years. There's a linux partition for mission specific code, the red team will have full access to this partition.

Q: Are you working on the wrong problem ? We don't have the talented people to make this commonplace, this isn't cool enough
A: Hacking demonstrations get people interested. The US government is considering mandating that all cars are networked, but this is dramatic
security issues, remote code execution

Q: Why is the hardware so constrained ?
A: It is very high scale, there's very small margins

Q: What did this cost, what this research want to do something differ to what management want ?
A; $70 million, we discuss but that why its good to NFS

Q: How much more expensive will high assures systems be ?
A: We don't know yet, that what we are working on. We focus on re-usability, open source, scale to reduce costs.

Q: What's the multi-core story ?
A: Its much more difficult to verify multi-core, bowing want single core, maybe they won't be available soon.

A: AR Don was started with an existing open source platform

Q: How will this approach scale
A: 80 % quadcoper is high assurance. But we don't even now how many CoL there is in car ? Linux is big, but we can add high assurance code progressively.

Q: How can insure and secure software co-exist ?
A: That's exactly what we are testing with the red team and linux partition.

Q: How do you know that hardware isn't vulnerable ?
A: Our project is big enough, other teams work this question.

Session 1: Domain Specific Languages I (Chair: Anil Madhavapedd)

Building Embedded Systems with Embedded DSLs (Experience Report)
Speaker: Patrick Hickey (Galois Inc)

A big thank you to kathleen

Goal: build a high assurance helicopter (quadcopter)

Challenges: Its an embedded systems, its high real
time system (1 millsec), system needs to be safe and secure.
Constrains: 3 engineers and 18 months, similar projects are 50K CoL.

There are billions of embedded systems, the 1 pence cost sensitivity it very high. Basically choice between C, C or C.
Typical secure issues, difficult to push a patch.

Approach: We would love to use Haskell or OCaml, but GC is not compatible with hard real-time. C or C++ (a subsec), a trade-off between safety and developer productivity.

Build your own tools, starting with a clean state language, correct by construction. We built a embedded DSL compiler in just a few month. The language is embedded in Haskell and compiles to C, its OS and called Ivory. Ivory is safe subset of C with memory & type system, no undefined behaviour. No heap, you can only allocate to the stack or global. We embedded in Haskell so we can utilise its type checker. Haskell is ivory macro language.

Modules are composed to expressions and global, there's a global name space.IMG_0002

Tower DSL - for organising whole applications. Build on top off ivory, to combine procedures into applications.

The quadcopter story - we have almost build all code in ivory. The bulk of the code is building drivers, that was about 10 kloc, the application was only 3 kloc. We needed to operate with existing the messaging format, we mangled some python code to generate ivory. Totally about 48 kloc.

The code was delivered to the red team, and now no undefined/buffer overflows etc.. There exists some broad system level issues.

This was a large complex application, but we build high assurance code very quickly. Embedding DSL in Haskell allowed us to build compositional programs.

Source code is at

Q: You and OCaml both target ARM, so why not us OCaml
A: We wanted compatibility with niche systems

Q: Did you run out of memory ? how do you communication this to the programmer ?
A: We don't currently have way to determine maximum stack depth, we are working on it.

Q: Correct of construction C code, you mean subset of C right ?
A: Yes, no pointer arithmetic

Q: Array handling
A: All arrays are static length

Q: What about error messages for embedded DSL's ?
A: It tough, and we would love to improve it

Q: How do you stop integer overflow ?
A: Using assertions instead of expressing in the type systems. The user can manually add annotations.

Q: Your re-targeted a python script to generate ivory instead of C, how many bugs did you find ?
A: Not sure, its was more clean slate

Concurrent NetCore: From Policies to Pipelines
Speaker: Cole Schlesinger (Princeton University)

IMG_0003Policy language for describing network policies. A quick intro to basic networking, we traditionally we use distributed protocols such as OSPF but now we have a centralized server to coordinate. We us openflow 1.0, each switch is a simple predicate and action table. There isn't just 1 table, different tables for many difference purposes e.g. different layer. Some switches have reconfigurable pipelines. We are now looking at OpenFlow 2.0 to switch into two phrases: configuration (like flashing router) and population.

We have developed a core calculus for pipelines, we have hardware models for 3 switch architectures and add virtual pipelines to our compiler.

The language:

example using the Broadcom trident switch, each table is a function from a packet to a set of packets

The syntax for an example pipeline (trident switch)
W;(all,{port,vport}) ; x:(dst MAC,port) ; if -(vport=1) then y:(vport,port) else id ; z:(all,all)

The control can connect to many switchers, the programmer can express a virtual pipeline as a minimum for each physical pipeline

example controller
m:(typ, port) + r:(dst MAC, port)

We developed 3 general techniques we designed for "RMT" architecture
1. Consolidate packet duplication
2. Refactor field modification
3. find field size

The syntax for the language, see paper. We are inspired by the frenetic family of languages.

We have added concurrency and thus race conditions, we using typing judgements for read and write permissions on each field.
Our normalisation lemma handles rules such as no overlap between read and write sets.

Q: Your plus operator seems to compile after parallelism
A: Its not compiled away just implicit



Session 2: Static Analysis (Chair: Ken Friis Larse)

SeLINQ: Tracking Information Across Application-Database Boundaries

Speaker: Daniel Schoepe (Chalmers University of Technology)

Motivation: Information flow control for computation & database queries. We given data to many complex application that we don't trust. Communication bounds better application is weak spot, so we consider end-to-end. Our contribution is end-to-end information flow with type checker and compiles to F#.

Typically we interface with SQL database, if we don't do it properly then there's lots if issues. Now people write code to generate inference code with SQL

Information flow code should into lead private data. Non- interference means private data channels shouldn't interact with public data channels. Language-integrated query using a toy language (subset of F#) with includes meta-programming. Security policies and security levels are expressed in the type system. We can then annotate databases columns, e.g. name is public but age is private.

Low equivalence relation is equivalence taking into consideration the new types for security levels. We can then use this relation to encode non-interference.

The type system is based on flowCaml, by Pottier and Simonet, 2003.

Our type checker and compiler implementation is written in Haskell using BNFC, we support ADT's .

Using a movie rental database as an example, we consider addresses as private but cities as public. We can query for all rental from a city with public but if we then try to get addresses, the type checker will reject this.

Q: Do you support more complex labelling schemes ?
A: Yes, we support lattices

Q: Can we classify fields or rows ?
A: No

Q: The exists function you demonstrated doesn't depend on type of elements, does it ?
A: No, but we can express that

Q: Do you deal with aggregation ?
A: We haven't considered that yet

Type-Based Parametric Analysis of Program Families
Speaker: Sheng Chen (Oregon State University)

A software system is a family of programs with a configuration, making error messages are difficult to read. We start with a program family, select a program and analysis but variational analysis takes a different route. We will look at the variational analysis today (VA):

Programming by search, applying an analysis gives us results R', but we expected R. We can instead introduce variations to data structures. e.g list -> variational list. Then we adapt analysis, create correctness proof and evaluate performance.

There's a lot of very helpful code extracts, syntax definitions etc.. that I didn't have time to copy, see the paper for them.

Q: In your performance results, did you examples fit into the more or less aggressive approach ?
A: They work even in the more aggressive example
We are breaking for lunch so we'll be back at 2:00

Session 3: Binding Structure (Chair: Tarmo Uustal)

Romeo: A System for More Flexible Binding-Safe Programming
Paul Stansifer (Northeastern University); Mitchell Wand (Northeastern University)

IMG_0005Programming is hard. Meta programming allows us to program to program, but I can introducing name issues. Alpha equivalent inputs, should produce alpha equivalent outputs. We have developed a binding safe programming language, with notation to express complex binding. Romeo expresses binding information in types.

Example remeo code:
stx = (Lamda (a) (+ a 1))

Romeo automatically renames a (as its not exposed) to makes sure no names clash.

Again, have a look at the paper for cool code examples

Romeo is a binding safe language that support term languages with complex binding structure

Q: In meta programming we want effects, how do deal with e.g. meta programming for loop invariant code motion?
A: Remeo is side-effect free, effects in an meta programming we'll take this offline

Q: Is this online ?
A: Not yet

Q: This is very similar to lamdba M ?
A: This was your inspiration but its not powerful enough as we have syntax cases

Q: Hygienic macro expansion ?
A: We indirectly mentioned it

Q: Thanks for getting starting with an example
A: Thanks

Q: What's the complexity of expansing remeo code ?
A: potentially n^2, we think we could perform the substitutions lazily to improve this

Q: can we represent practical thing ?
A: We don't have a story for possible racket macro's you could want, some are simple

Maximal Sharing in the Lambda Calculus with letrec
Speaker: Clemens Grabmayer (VU University Amsterdam)

People desire increased sharing, e.g. to reduce code duplication and check equality of unfolding semantics of programs.

IMG_00063 Steps:
- interpretation - turning lamdba-letrec into lambda term graphs
- bisimulation check & collapse of lambda let graphs -
- readback

Our tool is available on hackage
Possible extensions include adding support to full functional languages.

Q: How is this similar to disiding type equivalent in System F?
A: I will look into this

Q: Is this subsumed by Kleene algebra ?
A: I will look into this

Session 4: Program Optimisation (Chair: John Launchbury)

Practical and Effective Higher-Order Optimizations
Speaker: Lars Bergstrom (Mozilla Research)

Many CFA-based optimizations addressed in other ways, but higher-order inlining is the missing optimization. In practice, high performance functional programs avoid functions, e.g. OCaml compiler example.

What is the concrete function called from some computed function call ? (handled by 0CFA)

We have come up with a new analysis to see when can do higher-ordering inlining. We see if any free variables have been rebound between the closure capture location and the call site.

- Normalize and number soure code
- Build control-flow graph
- Add CFA-infromed edges
- Perform rebinding

Q: How can I as a programmer understand when the optimisation will kick in ?
A: If your the compiler writer, yes, otherwise we don't yet have a story for this

Q: What happens with things like and ?
A: This are often special case in the compiler to inline

Worker/Wrapper/Makes It/FasterIMG_0007
Speaker: Jennifer Hackett (University of Nottingham)

Research focus on correctness of optimisations but there's a lot less work on how we check that the transformation is actually an improvement.
Example of adding an accumulator to th
e List.rev function

The worker wrapper theroms captures induction.

I'm headiing off and direct the reader over to Leo at


ICFP 2014: Day 1

Posted by Leonhard Markert

This is day 1 of the 19th International Conference on Functional Programming.

Opening by the General Chair, Johan Jeuring

Quick facts about ICFP 2014:

  • 340 people registered for ICFP
  • 466 people registered for any event
  • most people are from Sweden, UK and USA
  • 21 sponsors, $60k in sponsoring

Keynote: Using Formal Methods to Enable More Secure Vehicles -- DARPA's HACMS Program

Kathleen Fisher (Tufts University)

Four and a half years long program, 2012--2017.

Many everyday objects today are computers! Infrastructure, medical devices, refrigerators, cars ... examples given for these things being hacked.

Focuses on cars: "computer on wheel" -- 100s of interconnected computing units. Everything is under control of software: braking, accelerating, locking.

First attack vector was via diagnostic bus but this requires physical control (you need to be in the car). Later found that physical control is not necessary to take over a car (Bluetooth, emergency number, audio CD).

Documentation for car systems exist and spare parts can be ordered online and analyzed (so no air gap, no security by obscurity).

Use the desktop computer approach? -- anti-virus scanning, intrusion detection systems, patching infrastructure. Problem: "security software" tends to introduce vulnerabilities. Also: embedded systems don't have the resources to monitor themselves all the time the way desktop computers do.

SAT solvers are getting faster; this allows us to use them to verify real software and hardware.

Clean-slate methods for high-assurance software: code synthesis, DSLs, Interactive Theorem Prover as PL.

Structure of the HACMS program: technical areas are vehicle experts, OS (building on verified sel4 kernel), control systems, research integration (management, composition: how to put high-assurance components together to create a bigger high-assurance component?, integration), red team (hints at problem of how to assess the security of a system -- currently a heuristic is: how long does it take for hackers to take over the system?).

Air team works on helicopter and quadcopter; ground team works on cars.

Demo video: WiFi-controlled quadcopter being taken over using aircrack-ng.

Every 18 months the red team gets access to the system and tries to find vulnerabilities.

Started with monolithic software, no RTOS, no security. After first iteration: use faster processor, put FreeRTOS and HAL under still monolithic Ardupilot software. Next iterations: generate more and more code from new DSL.

SMACCMCopter 18 months assessment: it flies! Air team can prove system-wide security properties like memory safety, ignoring malformed / non-authenticated messages; all good messages received will reach the motor controller. Red team found no security flaws in sex weeks with full access to source code. "Probably the most secure UAV on the planet" ("kinda scary").

Research integration (Rockwell Collins) used AADL to describe how hardware and software components fit together. Developed tools (AGREE, Resolute) to reason about the system. They are used to check assumptions about the system (example: there is only one way to communicate with the quadcopter).

Control systems (Galois) created Ivory and Tower, open-source EDSLs for synthesizing safe low-level code and tasks, respectively. Designed and built SMACCMCopter, the first high-assurance UAV software, in under two engineer-years.

OS (NICTA) proved full functional correctness of a fast OS microkernel, sel4 (now open source). Also working on eChronos, a high-assurance RTOS product line, and formally verified OS components (drivers, file system etc). CAmkES is a system for connecting and configuring these components.

Vehicle experts (Boeing) integrate HACMS technologies into the Unmanned Little Bird.

Air team in phase two tries to re-architecture (split) the system so it runs on a flight computer and a mission computer as is usual in real world scenarios. Red team will then try to gain control over the flight computer from the mission computer.

More detailed description of individual tasks follows.

Tech transition: a huge problem. The cyber-physical systems industry know they have a cyber-security problem but there are many barriers to adoption of HACMS-like technology.

C: Make it cool! Come up with a catastrophic event!
A: Well this is a response to "you can take over a car wirelessly". Another aspect: it will probably be mandated soon for cars to take to each other -- but remote code execution was never considered.

Q: Why not have quadcores in the car?
A: Very price-sensitive; the industry has tiny margins.

Q: How much does this project cost?
A: $70m.

Q: Research: top-down or bottom-up, which is better?
A: We need both. DARPA is bottom-up, NSF is top-down.

Q: You expect to pay more for high-assurance code. What is the cost? Especially for the second, third, ... project.
A: High-assurance code will always be more expensive. We hope to bring down the additional cost by developing tools that aid building high-assurance code. But have to consider: traditionally we pay people to write sloppy code; the cost from running sloppy code is never properly accounted for! Maybe high-assurance software is cheaper in the long run.

Q: Why did you replace the Arduino processor?
A: We wanted to start with an existing open source hardware platform.

Q: (bit unclear)
A: We're close to having one high-assurance system, the SMACCMCopter. Still far away from having a high-assurance car for example (one problem: no-one knows how many lines of code a normal car has!)

Q: How do you combine secure and unsecure code?
A: That's one of the challenges of course.

Q: How do you know the hardware does what you think it does?
A: In this project, we simply trusted the hardware. Other people are working on hardware issues.

Session 1: Domain Specific Languages I (Chair: Anil Madhavapeddy)

Building Embedded Systems with Embedded DSLs (Experience Report)

Patrick Hickey (presenting), Lee Pike, Trevor Elliott, James Bielman, John Lauchbury (Galois Inc)

Open source:

Goal: Build a high assurance quadcopter controller (long-term: helicopter controller). Small embedded system (microcontroller), hard real time, safe, secure. Had three engineers, 18 months, starting from scratch. Expected to write ~50kloc C/C++ code.

Embedded systems are everywhere. They're basically computers from the 80s: small and cheap. Development tools are from the 80s too (C). You get all the security flaws you'd expect -- buffer overflows etc. Can't push patches, and there are more attack surfaces than ever.

Approach. Haskell, OCaml? Problem: very resource limited system; GC imcompatible with hard real-time. C, C++? Tradeoff between safety and productivity -- e.g. NASA Jet Propulsion Lab writes high assurance C; extremely costly.

Alternative: build your own tools. Start with clean slate, use language approach, correct by construction. Built an embedded DSL in just a few months: Ivory, embedded in Haskell, compiles to safe subset of C. It's memory safe and allows no undefined or implementation defined behavior. No heap, only global and stack allocation.

Haskell type system guarantees properties of Ivory language programs; Haskell is Ivory's macro language.

Ivory: expressions are pure Haskell values; statements are effectful, embedded in monad. Examples for what has been embedded in the type system from the paper are given.

Ivory programs (modules) are a collection of top level C procedures and variables.

Tower DSL: organising entire applications. Composes Ivory procedures into applications. Initial problem: multithreading with message passing; code generation of low level primitives. Tower is implemented as a Haskell macro that generates Ivory programs. Tower contains concept of Tasks and Channels. It became the DSL describing software components.

(Side story: a third-party Python tool generating C output was modified to output Ivory code as well; this code could then be type checked to make sure it was correct. This way, the Python tool didn't need to be trusted.)

SMACCMPilot: 10kloc (and growing) of drivers, 3kloc application (stabilizing the quadcopter and flying), 10kloc message marshaling. Generates about 48kloc of correct-by-construction C.

Red team results: found no buffer overflow, no undefined behaviours, no denial of service. After five months they found two subtle architecture level bugs. Lifted the layer of abstraction: no need to worry about low-level problems, can concentrate on architecture.

Conclusion. Quickly and cheaply built a large, complex application; caught lots of errors early by Haskell type checking, could focus on application design; used Haskell as a macro system making it possible to build compositional programs.

Q: How is this an improvement over the OCaml code generator?
A: Cross-compilation was a specific concern, making sure the code runs on 8-bit, 16-bit, 32-bit architectures.

Q: Restricted amount of memory: how did you deal with that?
A: Don't yet have a way to assess the maximum stack use of a program. But we think it is solvable.

Q: You construct correct-by-construction code that is a subset of C, e.g. you don't do pointer arithmetic.
A: Yes.

Q: Do all arrays have a size known at compile time?
A: Yes.

Q: Type error messages were a problem?
A: Yes.

Q: You don't allow undefined behavior. How do you prevent overflows?
A: This is not done at the type level; instead we insert assertions in the C code (at the moment ~2500 assertions). Hope to analyze and discharge these assertions statically in the future.

Q: How about Rust?
A: Big fan. Rust was in its infancy when the project started two years ago. But when using Haskell we get type checking for our macros.

Concurrent NetCore: From Policies to Pipelines

Cole Schlesinger (presenting), Michael Greenberg, David Walker (Princeton University)

Review on networking: have an enterprise network with hosts, connected to a switch. In a SDN, the controller tells the switch which rules to use to deal with packets. Openflow 1.0 uses prioritised rules for this. Has predicates and actions. But switch hardware is not that simple: lots of tables in a switch pipeline. New configurable designs available now.

Openflow 2.0: splits interaction between controller and switch into two phases: 1. configuration (think flashing an FPGA); 2. population of the tables.

Contributions: core calculus for packet processing pipelines and controller policies; hardware models for three switch architectures; compilation algorithms for deploying general controller polices to target switch architectures.

Example: Broadcom "trident" switch. Each component can be thought of as function from packets sets of packets. (empty: drop, singleton: forward, multiple: e.g. flooding).

Have forwarding table; sequencing; if-statements over predicates; rewrite component.

Intel "flexpipe" supports reconfigurability. Each table can match on and write to every field. Also supports concurrency; introduce concurrency operator to model this.

What about the controller? Easiest for programmer to define virtual pipeline that describes the requirements.

New operator: packet duplication -- copy and run packet through multiple components.

Example: compilation to the barefoot "RMT" architecture -- three passes: 1. consolidate packet duplication (output ports must be known at the time of packet duplication, also need to add metadata to model processing as it would have taken place in the virtual pipeline); 2. refactor field modification; 3. place generated tables into physical tables (use commutativity of concurrency and other tricks -- the algorithm is a combination of brute force and dynamic programming).

Metatheory. Predicates form a Boolean algebra; grammar given for Predicates and Policies. Have a type system and small-step semantics.

Typing judgement of the concurrency operator prevents race conditions. Can prove normalization and confluence, in combination giving strong normalization (determinacy). Adequacy can be shown too.

Future work: implement the compiler and transformer on a real chipset.

Q: Does your system support encryption of packets inside the network, like IPSEC?
A: Yes and no -- you could think of encryption as an atomic action but it has not been fully worked out.

Q: Plus operator?
A: Plus operator is duplication. Semantics: both packets are sequentially processed by the remaining pipeline.

Session 2: Static Analysis (Chair: Ken Friis Larse)

SeLINQ: Tracking Information Across Application-Database Boundaries

Daniel Schoepe, Daniel Hedin, Andrei Sabelfeld (Chalmers University of Technology)

Motivation: confidential data is entrusted to many application which are composed of many components. Information leakage often occurs at boundaries, e.g. SQL injections. Need to look at confidentiality as end-to-end security to prevent attaches.

Contributions: track information flow for code using database queries. Security type system ensures noninterference and proof of soundness. Compiles to executable F# code; realistic demo.

Approach: Traditionally, attacks often occur at component boundary, e.g. SQL injections. Tierless approach: correct communication across boundary ensured at compile-time. Example: write everything in F#, some of which compiles to SQL.

Intuition: untrusted program shouldn't leak private data to third parties. Need to track flow of information through the program.

Noninterference: private inputs must not influence public outputs. Attacker doesn't learn anything about secrets by observing public behavior of the program.

Language-integrated query -- build queries via meta-programming. Example: LINQ in F#. Guarantees user input is sanitized and SQL is syntactically correct.

Idea: types annotated with security levels (L for public, H for private).

Security policy specified by giving a typing to each database. Code example given.

Introduce low-equivalence relation for values that are indistinguishable for the attacker. Defined structurally based on type; components with level L have to be equal.

Each secure program should be noninterfering with itself.

Security type system has soundness: if program is well typed then it is noninterfering.

Implementation: type checker (based on constraint solving and unification)and compiler implemented in Haskell using BNFC. Language compiles to executable F# code; compilation consists mostly of removing security types. Type system and soundness proofs allow user-defined algebraic datatypes.

Case study: near-realistic movie rental database. Want to protect names and exact addresses of customers and staff. Examples given of valid and invalid queries.

Summary: light-weight framework for tracking information flow; security type system ensures noninterference; proof-of-concept implementation given; extension to ADTs; case study.

Future work: extend to client code (JS).

Q: Can I make the security policy express things like "only certain people should be able to access this"
A: No, can't do this at the moment.

Q: Could this be embedded into a computational framework where one could assist the system in showing that some access is in fact safe when the type system's approximation cannot show it?
A: That would be an interesting extension.

Q: Do you deal with aggregation?
A: This has not been considered yet.

Type-Based Parametric Analysis of Program Families

Sheng Chen (presenting), Martin Erwig (Oregon State University)

Can often think of programs as "program families" which by configuration can be refined to single programs. But build & install may fail in unexpected ways. Trying all combinations of options is infeasible.

Idea: use variational analysis directly on the program family / variational program. Introduce a framework for doing variational analysis in the type system.

Notation for choice in variational programs introduced.

Program by search.

Creating variational analyses: 1. add variation data structures, e.g. lists become variational lists (VL, variation in vales and list structures); 2. adapt analysis, e.g. analysis of sort becomes analysis of VL sort; 3. correctness proof (tedious); 4. performance analysis

Example: variational typing rules shown.

This framework consists of annotations (sets), types and constraints. All three have extension points. Application and Choice typing rules are shown.

A constraint solving example is shown involving merging and splitting.

Instantiating the framework for variational 0CFA: no extensions required for annotations, types and constraints but rules need to be extended.

Correctness evaluation of the framework. Implemented Flyweight Java version of the framework and proved it correct.

Performance evaluation shows that the framework performs almost as good as manually lifted 0CFA analysis.

Q: Is the aggressive (faster) approach powerful enough to check types?
A: Yes.

Q: Have you tried to do effect analysis in this framework?
A: The framework should be expressive enough to do this but it hasn't been done yet.

Session 3: Binding Structure (Chair: Tarmo Uustal)

Romeo: A System for More Flexible Binding-Safe Programming

Paul Stansifer (presenting), Mitchell Wand (Northeastern University)

Introduction: metaprogramming allows us to automatically generate programs. Name binding is a problem -- example given. Recent survey found that 8 out of 9 metaprogramming systems had name binding problems.

Binding safety: alpha-equivalent inputs should produce alpha-equivalent outputs. In theory this works: FreshML, MetaML. But these only support simple binding forms like λ. Real languages have more than λ, e.g. let* in Racket, "local" construct in SML.

Contributions: a binding-safe PL, Romeo, with a notation to express complex binding, clear big-step semantics, proof of correctness.

Explains the concepts of "imports" and "exports" in the context of name binding.

In Romeo, binding information is in the types.

What makes two let*s alpha-equivalent? Follow-up: when are two Romeo declarations alpha-equivalent? -- must have exactly the same behavior. Thus their exported binders must be the same.

Romeo's runtime enforces safety. It automatically avoids name clashes. Accidentally unbound names are a dynamic error ... which Romeo's optional deduction system can statically rule out.

Conclusion: Romeo is a binding-safe language that supports term languages with complex binding structure.

Q: What about de Bruijn indices? Effects in the metalanguage?
A: Romeo is side-effect free.

Q: Can I play with Romeo?
A: I'll try to convince my supervisor to release the source code. Currently the implementation is out of sync with the formalism.

Q: This looks similar to λ-m.
A: Lambda-m is not powerful enough. We give you syntax case.

C: Thanks for starting with a concrete example.
A: Thanks to my peers for the criticism!

Q: How does hygienic macro expansion relate to this?
A: If your macro gets larger and larger, there is less safety -- hygienic macros are all about protecting macros from each other.

Q: Complexity of expanding Romeo code?
A: O squared, so that's not good. Macro systems have the same problem though. Trick: perform actual substitution lazily, and we think the same technique could be used with Romeo also.

Q: How much expressiveness does Romeo have?
A: Can we express all Racket macros -- probably, we think most are just simple extensions of what we have right now.

Maximal Sharing in the Lambda Calculus with letrec

Clemens Grabmayer (presenting, VU University Amsterdam), Jan Rochel (Universiteit Utrecht)

Motivation: increasing sharing is desirable -- compact code, avoid duplicated work at run-time, enables us to check equality of unfolding semantics of programs.

1. interpretation of λ-letrec terms as term graphs; 2. bisimilarity and bisimulation collapse of λ term graphs; 3. readback of λ term graphs.

This method extends common subexpression elimination and is targeted at maximizing sharing statically.

Example of unfolding equivalence of fixpoint operator.

Example of graph interpretation given. First we generate a λ higher order term graphs. Transform into first-order term graph with scope vertices with backlinks, then into λ term graph.

Then we do a bisimulation check between λ term graphs.

The class of eager-scope λ term graphs is closed under functional bisimilarity.

Implementation: "maxsharing" on Hackage, uses Utrecht University Attribute Grammar Compiler.

Time complexity analysis of the individual steps follows.

Possible extensions: support for full functional languages (would require work on a core language with constructors and case statements); prevent space leaks caused by disadvantageous sharing.

Applications: use maximal sharing at run-time, possibly directly on supercombinator graphs; can be coupled with GC; code improvement (statically) to detect code duplication; checking function equivalence (relevant for proof assistants, theorem provers, dependently-typed PLs).

Q: Explicit weakening. Have you tried to implement reduction of the first order term graph?
A: It's possible. Used port graphs to implement reduction because that seemed easier.

Q: Does this subsume Kleene algebra?
A: Maybe? Interesting idea.

Session 4: Program Optimisation (Chair: John Launchbury)

Practical and Effective Higher-Order Optimizations

Lars Bergstrom (presenting, Mozilla Research), Matthew Fluet (Rochester Institute of Technology), John Reppy University of Chicago), Nora Sandler (University of Chicago), Matthew Le (Rochester Institute of Technology)

Control flow analysis (CFA) allows higher-order inlining. In practice, high performance function programs avoid functions.

Two subproblems: 1. what is the concrete function called from some computed function call? -- already handled by 0CFA; 2. does the potential inlining point have a compatible environment? -- "reflow", JITs.

Contribution: determine when higher-order inlining is safe. Key insight: don't check "is the environment the same?" but "could any of the free variables have been rebound between the closure capture location and the call site / potential inlining point?". This supersedes ad-hoc higher-order inlining tricks.

Technique: 1. normalize and number source code (annotations for variable binding sites); 2. build control-flow graph; 3. add CFA-informed edges; 4. Perform rebinding search on paths between closure capture location and potential inlining point

Safe and unsafe example given; control flow graphs shown.

Reflow analysis is strictly stronger. But this analysis has about 3% time cost of the entire compilation process.

Conclusion: start using closures in benchmarks!

Higher-order inlining heuristics still missing; correctness proof still incomplete.

Q (SPJ): You showed that the analysis is cheap but what do we get?
A: Benchmarks are in the paper.

Q: If I want to use this: is it clear for me where the optimization will kick in?
A: Well, yes, if you're the compiler writer (laughs from the audience). But this is a problem indeed.

Q: I usually pass closures to "map" and "fold", so these will have many call sites. Can this still be optimized in this framework?
A: "map" and "fold" are usually handled in a special way by the compiler anyway.

Q: Is it imaginable to prove that the optimization actually makes the program run faster?
A: Next presentation will try to answer this. But it's very difficult.

Q: What's the relationship to Milton's optimizations?
A: Not entirely sure. Is defunctionalization or CFA-style analysis better? An interesting question.

Worker/Wrapper/Makes It/Faster

Jennifer Hackett (presenting), Graham Hutton (University of Nottingham)

Context: lots of research into optimization focuses on correctness: is meaning preserved?; comparatively little work on improvement -- ensuring programs become "better".

Example: list reversal function. How can we prove that the improved program is both faithful to the original version and also more efficient?

The Worker/Wrapper transformation: Original program has type A, worker has type B, wrapper has type B -> A.

Formally, given

f : A -> A, g : B -> B, abs : B -> A, rep : A -> B


abs . rep = id, g . rep = rep . f


fix f = abs (fix g)

Reasoning about efficiency. Need some way to compare the cost of two programs. Naive approach: count steps taken to evaluate terms to some normal form. But this fails in a lazy context! Need precongruence.

Precongruence: a preorder and a congruence -- this excludes negative interaction between improvements at different points in the program.

Improvement theory: counting steps, also have a notion of improvement within a constant factor.

Improvement induction principle shown. "tick" idea. It's like guarded coinduction.

Shows the Worker/Wrapper theorem for improvement theory (typeless).

Structure of improvement proofs is essentially the same as that of correctness proofs.

Conclusions: we can make rigorous arguments about the efficiency of lazy programs; make performance guarantees for a general-purpose optimization.

Further work: structured recursion; turn it into a typed theory (advantage: only need to worry about contexts where the types make sense); look at improvement of space usage and other resources; quantify improvement (how much faster? which optimization should be applied if there are alternatives?); automate it.

Q: Do you have completeness results?
A: It's not clear whether Worker/Wrapper improvement proofs can be translated from fixpoint style to improvement theory.

Q: Do you have any examples where Worker/Wrapper transformation makes things slower?
A: Of course we have come across these, but we wouldn't put that in the paper! (Laughs from the audience). [Tree reversal example follows]

Q: Problem in GHC is the cost of building closures. How is this reflected in your framework?
A: Read early papers on improvement theory.

Q: How about local improvements?
A: This work does not look at the surroundings.

Q: Would things change dramatically if you applied this to a call by value language?
A: There has been some work on that.

Session 5: Context Dependence (Chair: Yukiyoshi Kameyama)

Compositional Semantics for Composable Continuations: From Abortive to Delimited Control

Paul Downen, Zena M. Ariola (University of Oregon)

Classical control: callcc is the classic control operator, going back to Scheme. Corresponds to classical logic (the way the λ-calculus corresponds to intuitionist logic).

Delimited control: delimit the scope of effects; continuations compose like functions. These little changes make delimited control vastly more expressive than classical control.

λ-calculus + callcc operator and λμ-calculus are equivalent. Relaxing λμ syntactically gives Λμ which allows delimited control! Λμ in turn is equivalent to lambda + shift0 + reset0.

Classical control in detail. Operational semantics of callcc as an extension of CBV lambda-calculus given. Alternatively, we can understand callcc using an equational theory. But: it's weaker than operational semantics. Some programs can be evaluated to a value but equational theory for callcc cannot reach a value! How can we know that we have the whole context?

Calling a continuation never returns -- it "jumps". A jump is the same when inside a larger evaluation context so it delimits the usable extent of a continuation.

λμ syntactically distinguishes jumps as "commands".

Relaxing the syntax. Λμ collapses term/command distinction. It is interpreted with the same rules only that we have more expressive meta-variables. No new constructs, no new rules. As typed calculi, Λμ is considered equivalent to Parigot's λμ. What happens if you ignore types?

shift and reset: common basis for delimited control. Continuations return and can be composed like functions.

λ-calculus + shift + reset is a true subset of Λμ. What's missing?

Difference between shift and shift0: the latter removes its surrounding delimiter.

λ-calculus + shift0 + reset0 is equivalent to Λμ.

Λμ as a framework for delimited control gives provable observational guarantees about the operators, e.g. idempotency of reset.

More in the paper: can get nice reasoning principles using call-by-value, call-by-name, call-by-need; equational correspondence with compositional transformations.

Q: What do you mean by equality in this presentation?
A: Observational equality -- this is about tools for reasoning about programs rather than expressivity of the calculi.

Q: λμ has a well known type system but to introduce delimited control you had to relax the syntax. Is there a type system for this?
A: (?)

Q: Classical logic, intuitionist logic -- delimited logic?
A: YES! Should investigate this.

Coeffects: A Calculus of Context-Dependent Computation

Tomas Petricek (presenting), Dominic Orchard, Alan Mycroft (University of Cambridge)

Coeffects is the dual of Effects. Done!

This talk is about the context in a type judgement.

Effect systems: Γ :- e : τ & σ
Coeffect : Γ & x :- e : τ

Interesting properties of Γ ("view from the extreme left"): variable related -- liveness, bounded linear logic, data-flow programming, provenance; environment related -- implicit parameters, type classes, distributed computing, platform versioning.

Coeffect calculus for bounded reuse: annotate each variable in the environment with the number of its uses in the expression, essentially tracking the number of variable uses.

Variable, abstraction and contraction rules shown.

Another possibility: coeffect calculus with historical values. Replace addition with max operation in the contraction rule.

Environment related: coeffect calculus for implicit parameters. Example: implicit parameter for time zone and time. We can calculate how many parameters are still required (i.e. not given by the current environment). The corresponding rule is non-deterministic.

What's in the paper? Unified system: coeffect scalar structure (a generalized semiring structure) and shape-indexed coeffects (context splitting and merging, per-variable or whole-context). Also have monadic and comonadic semantics -- not simple as λ-calculus is asymmetric (many to one)!

Why do coeffects matter? Generalize interesting systems (liveness, data-flow, implicit parameters, type classes) and (indexed) comonads are cool.

Q: What happens if you have (co)monads in the classical calculus?
A: Interesting question.

Program Chair's Report

Quick paper stats:

28 accepted papers out of 97 submissions.

2/9 functional pearls and 2/3 experience reports accepted.

299 reviews -- at least three per paper.

72 hour author response period (as usual).

It was decided not to allow papers from members of the program committee.

Comparison with previous years: number of papers approximately stable, share of accepted papers even more so.

Functional pearls are accepted less than normal papers.

Author's location: USA, UK, France, Japan, Netherlands, Sweden, Germany, other.

UK has highest acceptance rate, then USA.

Papers submitted only a couple of hours before the deadline were less likely to be accepted -- before that there seems to be no correlation between submission time and acceptance probability.

Big thanks to the program committee!

Deputy Lord Mayer of Gothenburg: invitation for drinks, short history of the city.


ICFP 2014: Day 0 (WGP)

Posted by Leonhard Markert

The 19th International Conference on Functional Programming will begin tomorrow. Two affiliated events, the 10th Workshop on Generic Programming (WGP) and the Workshop on Higher-Order Programming with Effects (HOPE), take place today.

WGP Session 1

Invited talk: Functional Programming, Object-Oriented Programming and Algebras

Bruno Oliveira (University of Hong Kong)

How to achieve modularity, type-safety and reuse without requiring a PhD in type theory? Bruno Oliveira proposes algebras as an alternative to algebraic datatypes and OO hierarchies, more specifically: variants of F-Algebras. Fold Algebras (using products of functions) and F-Algebras (using sums of products) are isomorphic.

An example of encoding generalized algebraic datatypes (GADTs) using type classes in a way similar to Church encodings due to Hinze from 2003, when there were no GADTs in Haskell.

Similarly, in OO, Church encodings were used to model the visitor pattern.

The Expression Problem: a problem of modularity. Given an existing type of expressions, how do you extend it (e.g. how do you extend data Exp = Lit Int | Add Exp Exp with multiplication)?

Solution 1: encode algebras as type classes, extend using subclasses.

Solution 2: use F-Algebras (Datatypes à la Carte)

In OO languages, can also solve the Expression Problem with algebras: use interface inheritance.

All current solutions using algebras still require heavy encodings. In the future: programming language support for algebras?

WGP Session 2

Generic Constructors and Eliminators from Descriptions

Larry Diehl (presenting), Tim Sheard (Portland State University)

Abstract: Dependently typed languages with an “open” type theory introduce new datatypes using an axiomatic approach. Each new datatype introduces axioms for constructing values of the datatype, and an elimination axiom (which we call the standard eliminator) for consuming such values. In a “closed” type theory a single introduction rule primitive and a single elimination rule primitive can be used for all datatypes, without adding axioms to the theory.

Abstract continued: We review a closed type theory, specified as an A GDA program, that uses descriptions for datatype construction. Descriptions make datatype definitions first class values, but writing programs using such datatypes requires low-level understanding of how the datatypes are encoded in terms of descriptions. In this work we derive constructors and standard eliminators, by defining generic functions parameterized by a description. Our generic type theory constructions are defined as generic wrappers around the closed type theory primitives, which are themselves generic functions in the A GDA model. Thus, we allow users to write programs in the model without understanding the details of the description-based encoding of datatypes, by using open type theory constructions as an internal domain-specific language (IDSL).

Ornaments in Practice

Thomas Williams (presenting), Pierre-Évariste Dagand, Didier Rémy (INRIA)

We often write very similar functions for structurally similar data types, e.g. "add" for natural numbers and "append" for lists. Natural numbers and lists have the same recursive structure and a coherent property, a "length" attribute:

add (length ml) (length nl) = length (append ml nl)

A simple projection function exists from lists to natural numbers: the "length" function.

The motivation for this paper was to add ornaments to ML. Naturals/lists example: an ornament is defined by a projection function from the ornamented type to the bare type, e.g. "length". Declare an ornament as

ornament from length : a list -> nat

The system checks whether recursive structure is preserved.

Syntactic lifting is how "append" can be derived from "add" almost automatically. Given

let rec add m n = match m with
  | Z -> n
  | S m' -> S (add m' n)

and the lifting expression

let lifting append from add with
  {length} -> {length} -> {length}

the system can derive

let rec append ml nl = match ml with
  | Nil -> nl
  | Cons(x, ml') -> Cons(?, append ml' nl)

But the ? is ambiguous (it's the "creative part") and needs to be completed manually, by code inference (heuristically) or a "patch".

A patch would look like

let append from add
  with {length} -> {length} -> {length}
  patch fun _ -> match _ with Cons(x, _) -> Cons({x}, _)

Applications of this: for refactoring (see question further down), or to derive an evaluation function (uniquely) from "conv", a bijective function between expression types.

More complex data structures can be lifted too (e.g. Sets and Maps); so can higher-order functions. Lifting for GADTs is also possible and is automatic for some invariants given the expected type of the function.

Conclusions: describing ornaments by projection is a good fit for ML, syntactic lifting gives good predictable results. Future work: better patches, integration into ML, combining ornaments.

Q: Projections seem very similar to abstraction functions in abstract interpretation.
A: Could be seen as abstract interpretation.

Q: Is this weaker than what was presented in the 2012 paper? There you could compute ornaments out of indices.
A: Indices flow in the other direction in the presented approach.

Q: Refactoring example. Normally want to throw old code away and keep new code. Here we get some conversion between old and new.
A: Yes, could remove old code.

Q: How about code maintenance?
A: Patches are part of the answer.

Q: Can the patch language also talk about the logical parts?
A: That would not be easy.

Type Inference for the Spine View of Data

Matthew Roberts (presenting), Tony Sloane (Macquarie University)

Context: rho-Calculus (theory of term rewriting), Pattern Calculus and bondi, Stratego, "Scrap your Boilerplate" (SYB paper), kiama, Reloaded/Revolutions.

Motivation: precisely characterize the type inference machinery needed to support the spine view of data.

Result: need to add "ispair" expression (see further down) to first-class polymorphism (FCP), which is a small extension of Hindley-Milner.

Fully applied constructor view means data equals constructor with all arguments (fully applied). When arguments are missing this is a function, so we can't pattern match against it.

Spine view only knows nodes of arity two or zero. This means that all pattern matching can be done using

ispair d bind (x, y) in e1 else e2

which is morally equivalent to

case c of o x y -> e1
          _     -> e2

Here x refers to the constructor and all its arguments except the last one, and y is the last argument.

Examples: generic map and fold.

Implementations: the "decidedly generic compiler" dgen and "kiama".

Q: Why is complete type inference so important?
A: The semantics of doing spine view with only one construct took many attempts; this one allows for type inference so it was chosen over the others.

Q: Have you tried to use FCP on other spine view representations?
A: With FCP, you trade off type annotations with data annotations.

WGP Session 3

First-class Isomorphic Specialization by Staged Evaluation

Alexander Slesarenko (presenting), Alexander Filippov, Alexey Romanov (Huawei Technologies)

There are three types of program specialization: partial evaluation, supercompilation and isomorphic specialization. Idea: we have A, B, P such that P : A -> B; also A', B' which are isomorphic to A and B, respectively, and P' : A' -> B' which can be computed more efficiently than P. Potentially there are multiple options for A', B' and P'. How can we use P' on A and B? (A and B: 'domain' data; A', B': 'core' data)

The example used is matrix-vector multiplication. Matrices, for example, can be represented as dense (vector of vectors), This is implemented as an EDSL (embedded domain specific language) in Scala. Here we can abstract over dense and sparse vectors and matrices. The specialization can take place at runtime, and we can allow for alternative specialized versions. Specialization can lead to faster code.

Generic composition of isomorphisms -- idea: build isomorphisms for each constructor in the core language. These isomorphisms are first class citizens of the framework.

How it works: staged method invocation with graphs.

Take-home agenda:

  1. Develop algorithm in EDSL
  2. Identify isomorphic representations in the domain language
  3. Implement ADTs using a core language and isomorphic representations
  4. Generate representation-specific implementations in the core language

Implementation can be found at

Q: How did you proceed? Paper includes theory and implementation notes
A: Original idea was to use graphs, that was the starting point

Q: You talk about large performance increases compared to original Scala implementation. Have you compared with C/C++?
A: No.

Algebraic Effects and Effect Handlers for Idioms and Arrows

Sam Lindley (University of Edinburgh)

Idea due to Plotkin: algebraic effects describe abstract computations. Abstract computations are trees (modulo equations).

Monad trees are unrestricted abstract computation trees (dynamic control and data flow). Arrow trees are monad trees where only static control flow is allowed (static control flow, dynamic data flow). Idiom trees (idiom = applicative functor) are arrow trees where only static data flow is allowed (static control and data flow).

Examples are given for each monad, arrow, and idiom; the control flow and data flow trees show the dynamic / static nature.

Static computations are useful as they allow optimisations and low-level implementations (arrows correspond approximately to circuits, for example).

Key contribution: using Flow Effects, we can write monad/arrow/idiom code in a single style -- this is very different from Haskell, where different notations exist for each.

A handler is an interpreter for an abstract computation. It is defined as a fold over a computation tree, specifying how return values and operations are interpreted. Given a monad handler, we can derive an arrow and idiom handler.

Q: Can it be inferred from a Flow Effects expression whether the computation it defines is a monad, an arrow or an idiom?
A: Yes.

Q: What's the status of the Flow Effects syntax?
A: Not finalised yet -- the syntax shown in the paper is a "mathematical" syntax with braces, indices etc.

A short discussion followed on whether a unifying notation is a good idea -- maybe it is a good thing that we can tell from the syntax whether a computation is a monad, an arrow or an idiom?

Scoping Rules on a Platter -- A Framework for Understanding and Specifying Name Binding

Larisse Voufo, Marcin Zalewski (presenting), Andrew Lumsdaine (Indiana University)

Name binding: for a given reference (use), where is the corresponding declaration (what it refers to)?

In order to understand and unambiguously define name binding rules, a number of name binding combinators has been developed.

Scope combinators are: hiding, merging, opening, weak hiding.

Together they provide a language that describes how scopes are combined to look up a reference. These are then used to describe how a specific language does name binding. The example is C++: the standard devotes over 60 pages in total to name binding rules (and it turns out both Clang and GCC get operator resolution wrong).

Discussant: Ilya Sergey

Q: Could we use attribute grammars for name binding?
A: Finding a grammar for C++ has been attempted many times... It's hard to take a principled approach and applying to C++.

Q: Did you find inconsistencies in the C++ standard when you tried to collect the scoping rules?
A: No, to our surprise -- it's complex but consistent.

Q: Did you come up with any ideas to simplify the rules along the way?
A: There don't seem to be any ways to simplify the rules (except using a different language).

WGP Session 4

Composing and Decomposing Data Types -- A Closed Type Families Implementation of Data Types à la Carte

Patrick Bahr (University of Copenhagen)

This paper was inspired by the recent addition of closed type families to the Glasgow Haskell Compiler (GHC). Closed type families were used to implement Data Types à la Carte, specifically the subtyping constraint :<:

Data Types à la Carte -- idea: decompose data types into two-level types. Express recursive data types as the fixpoint of a functor. These functors can be combined by coproduct construction :+:.


-- recursive data type
data Exp = Val Int | Add Exp Exp

-- as fixpoint of a functor
data Arith a = Val Int | Add a a
type Exp = Fix Arith

-- combine by coproduct construction
data Mul a = Mul a a
type Exp' = Fix (Arith :+: Mul)

The subtyping constraint is easily expressed as a type class with an injection function inj and a projection function prj. But this approach treats :+: asymmetrically, doesn't inspect the left-hand side and is ambiguous.

Contribution of this paper: a re-implementation of :<: which behaves as expected (f :<: g <==> there exists a unique injection from f to g), avoids ambiguous subtyping (by rejecting multiple occurrences of signatures) and allows expressing isomorphism.

Implementation: Have a type-level function Embed taking signatures f, g as arguments. Produces proof object p for f :<<: g -- a relaxed version of :<: allowing ambiguity -- and checks whether p proves f :<: g. We can then derive implementations of inj and prj using the proof object as an oracle in instance declarations.

The code for Embed, Sub and helper type classes is shown. It's all contained in the "compdata" package.

Caveats: unhelpful error messages; compile time performance is unpredictable.

Discussant: Wouter Swierstra

With this approach we gain lots of expressive power, e.g. control over how the search proceeds, at the price of more type level programming.

C: A solution for the error messages apparently exists. Also, Idris does this really nicely (it's also much more complicated).

Q: Motivation: want to use sums of products. Interesting tradeoff by maybe focusing on coproducts of products?
A: ?

Q: Would instance chains have solved your problem?
A: Yes.

Q: All this type hackery for deriving injections and projections -- how about just providing them every time?
A: Of course you can do that, but it's inconvenient and obvious from the type. If you need more flexibility, you can still do it manually.

Q: No canonical ordering for injection (eg sum of int and bool) -- is this a problem?
A: Yes, at the moment you have to annotate.

True Sums of Products

Edsko de Vries, Andres Löh (presenting) (Well-Typed LLP)

A new approach to datatype-generic programming in Haskell; notable features: faithful representation of datatypes as n-ary sums of n-ary products; a library of high-level combinators to encourage concise and reusable functions; separation of metadata in the representation.

Available on Hackage as "generics-sop". "basic-sop", "lens-sop" and "json-sop" provide examples, generic lenses and generic JSON (de)serialization respectively.

Motivation was to improve on existing models: GHC.Generics representation is too constrained (binary sums and products) but at the same time too flexible (arbitrary nesting). Also has noisy metadata everywhere. Functions end up making implicit assumptions about the structure of the data which is not guaranteed by the type system.

Discussant: Patrik Janssen

Q: What kind of code does this generate?
A: Not looked at yet. But lots of implicit arguments are potentially being passed around at runtime.

Q: Can this be done better in Agda?
A: Library was completely developed in Haskell. Can probably be done more nicely in Agda, but in this case the development was driven by an actual need to deal with certain generics problems in Haskell.

Q: n-ary lists are left-biased: don't you run into similar performance problems as with binary representation?
A: List-based representation gives uniform encoding for all constructors. But probably not ideal for data types with thousands of constructors.


Live-blog from CoNEXT 2013 – day 1

Posted by aa535

Kicking off the conference with some explanations about how the TPC selected the papers. The program looks great and I will be covering some of the sessions here over the next three days.


Live-blog from SenSys 2013 – Day 1

Posted by Aaron

The 11th ACM Conference on Embedded Networked Sensor Systems (SenSys 2013) just kicked off, held at the University of Rome 'La Sapienza' in Rome. From Cambridge, Cecilia is chairing the session "Sensing People" and the SenSys 2013 Doctoral Colloquium.

Some facts about SenSys 2013:

  • 250+ attendees
  • 1/3 are students
  • 40% have registered for both the conference and the workshops

For submission:

  • 21 papers got accepted out of 123 submissions (17%)
  • most of them received 3 to 8 reviews
  • 70 presentations in the poster and demo track - each one also got a 1-min madness talk during the main conference

There is an interesting opening talk by the chairs showing some stats about the submission this year (as far as I can recall)

  • register the paper earlier, and submit it late (close to the DL) - this holds for most of the accepted ones
  • most of the accepted papers are from America, around 50%
  • certain key words in the title will lead to rejection: nodes, networking, vehicle, etc. (I should have taken a photo while laughing at that slide.. :(

Since there is no electro-plug in the conference room. Most of notes are on the paper. Will find time to move them here.

Keynote by Shahram Izadi from MSR Cambridge: "The New Face of the User Interface"

Introduce the work carried out at MSR Cambridge by the Human-Computer-Interaction (HCI) group, including the 3D interaction of physical and digital objects, 3D model reconstruction using smartphones (using the Queens' College and the Mathematics bridge in the demo). They highlight how to design using low price component and smartphones.

Session 1 - Communication Systems

Chaos: Versatile and Efficient All-to-All Data Sharing and In-Network Processing at Scale

Olaf Landsiedel (Chalmers University of Technology, Sweden), Federico Ferrari (ETH Zurich, Switzerland), Marco Zimmerling (ETH Zurich, Switzerland)

Q: Mainly evaluated by simulation, will it be a gap if the design is applied to real environment?

A: Not yet tested in real environment, hence we don't know.

Let the Tree Bloom: Scalable Opportunistic Routing with ORPL

Simon Duquennoy (SICS Swedish ICT AB, Sweden), Olaf Landsiedel (Chalmers University of Technology, Sweden), Thiemo Voigt (SICS Swedish ICT AB and Uppsala University, Sweden)

Practical Error Correction for Resource-Constrained Wireless Networks: Unlocking the Full Power of the CRC

Travis Mandel (University of Washington), Jens Mache (Lewis & Clark College)

Use CRC to correct wireless transmission errors, solving the reliable issue by their design TVA, Transmit-Verify-Ack.

Very good presentation, and presenter ends his talk with a song about CRC :)

Session 2 - Sensing People

iSleep: Unobtrusive Sleep Quality Monitoring using Smartphones

Tian Hao (Michigan State University), Guoliang Xing (Michigan State University), Gang Zhou (College of William and Mary)

Using sound monitoring to infer sleeping quality.

Lifestreams: a modular sense-making toolset for identifying important patterns from everyday life

Cheng-Kang Hsieh (UCLA CSD), Hongsuda Tangmunarunkit (UCLA CSD), Faisal Alquaddoomi (UCLA CSD), John Jenkins (Cornell Tech), Jinha Kang (UCLA CSD), Cameron Ketcham (Cornell Tech), Brent Longstaff (UCLA CSD), Joshua Selsky (Cornell Tech), Betta Dawson (UCLA CSD), Dallas Swendeman (UCLA David Geffen School of Medicine Department of Psychiatry and Biobehavioral Sciences), Deborah Estrin (Cornell Tech), Nithya Ramanathan (UCLA CSD)

Probably the longest author list in SenSys history.

14GB data collected for 44 young mothers.

Q: How do you recruit patients?

A: Done by the partners in hospital.

Q: Any new findings from the data?

A: We detect some hidden behaviors not noticed by the patients, e.g., unusual long walking outdoors, due to anxiety.

Diary: From GPS Signals to a Text-Searchable Diary

Dan Feldman (MIT), Andrew Sugaya (MIT), Cynthia Sung (MIT), Daniela Rus (MIT)


Live-blog from SOSP 2013 – Day 2

Posted by Ionel Gog

The second day of the 24th ACM Symposium on Operating Systems Principles has just started.  Several of us are here and will be live-blogging the talks and Q&A sessions.