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Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
ISTC-CC provides a listing of useful benchmarks for cloud computing.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
ISTC-CC Abstract
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Neural Information Processing Systems Conference (NIPS’13), December 2013.
Qirong Ho, James Cipar, Henggang Cui*, Jin Kyu Kim, Seunghak Lee,
Phillip B. Gibbons^, Garth A. Gibson, Gregory R. Ganger*, Eric P. Xing
School of Computer Science,
Carnegie Mellon University
*Electrical and Computer Engineering,
Carnegie Mellon University
^Intel Labs
We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees. The parameter server provides an easy-to-use shared interface for read/write access to an ML model's values (parameters and variables), and the SSP model allows distributed workers to read older, stale versions of these values from a local cache, instead of waiting to get them from a central storage. This significantly increases the proportion of time workers spend computing, as opposed to waiting. Furthermore, the SSP model ensures ML algorithm correctness by limiting the maximum age of the stale values. We provide a proof of correctness under SSP, as well as empirical results demonstrating that the SSP model achieves faster algorithm convergence on several different ML problems, compared to fully-synchronous and asynchronous schemes.
