some papers caught my eye include:
\nSTRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning.
\nlooks quite clever - not sure how it would work for a bayesian inferencer, but made me think<\/p>\n
Increasing Large-Scale Data Center Capacity by Statistical Power
\nuses MORE servers to reduce power - clever control theory approach - Baidu traces to eval is real, large system<\/p>\n
A High Performance File System for Non-Volatile Main Memory.
\nseems solid<\/p>\n
Crayon: Saving Power through Shape and Color Approximation on Next-Generation Displays.
\nneat, but niche - OLED laptop displays consume less power if you render stuff cleverly - nice bit of human factors driven algorithm design to minimise impact on perceived image quality - gets 56% power saving on tablet with little subjective impact <\/p>\n
A Study of Modern Linux API Usage and Compatibility: What to Support When You're Supporting.
\nBest paper award - nice talk - fun....<\/p>\n
BB: Booting Booster for Consumer Electronics with Modern OS.
\nbasically, Samsuung's smart TV boots a lot faster coz they hacked it a lot. (I have one, and replaced an LG with it, and its true:)<\/p>\n
TFC: Token Flow Control in Data Center Networks.
\nis basically isarhythmic flow control (an idea from donald davies 1960s packet switched networking) done right:)<\/p>\n
JUGGLER: A Practical Reordering Resilient Network Stack for Datacenters.
\nuses offload engine and other stuff to do a very solid job of dealing with putting packets in right order for TCP (where out-of-order delivery was caused by load balancers)<\/p>\n
Flash Storage Disaggregation.
\nwhat it says on the tin<\/p>\n
Shared Address Translation Revisited. evil question about reverse page->structure mapping in linux - how to figure out which process to go to with shared stuff...<\/p>\n
POSIX Abstractions in Modern Operating Systems: The Old, the New, and the Missing. - hopelessly optimistic but engaging speaker:)<\/p>\n
All findable via
\nhttp:\/\/eurosys16.doc.ic.ac.uk\/program\/program\/<\/p>\n","protected":false},"excerpt":{"rendered":"
some papers caught my eye include: STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning. looks quite clever – not sure how it would work for a bayesian inferencer, but made me think Increasing Large-Scale Data Center Capacity by Statistical Power uses MORE servers to reduce power – clever control theory approach – Baidu […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/posts\/1905"}],"collection":[{"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/comments?post=1905"}],"version-history":[{"count":1,"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/posts\/1905\/revisions"}],"predecessor-version":[{"id":1906,"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/posts\/1905\/revisions\/1906"}],"wp:attachment":[{"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/media?parent=1905"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/categories?post=1905"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.syslog.cl.cam.ac.uk\/wp-json\/wp\/v2\/tags?post=1905"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}