Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
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.
Exploiting Iterative-ness for Parallel ML Computations
Proceedings of ACM Symposium on Cloud Computing 2014 (SOCC'14), November 2014.
Henggang Cui, Alexey Tumanov, Jinliang Wei, Lianghong Xu, Wei Dai,
Jesse Haber-Kucharsky, Qirong Ho, Gregory R. Ganger, Phillip B. Gibbons*,
Garth A. Gibson, Eric P. Xing
Carnegie Mellon University
Many large-scale machine learning (ML) applications use iterative algorithms to converge on parameter values that make the chosen model fit the input data. Often, this approach results in the same sequence of accesses to parameters repeating each iteration. This paper shows that these repeating patterns can and should be exploited to improve the efficiency of the parallel and distributed ML applications that will be a mainstay in cloud computing environments. Focusing on the increasingly popular “parameter server” approach to sharing model parameters among worker threads, we describe and demonstrate how the repeating patterns can be exploited. Examples include replacing dynamic cache and server structures with static pre-serialized structures, informing prefetch and partitioning decisions, and determining which data should be cached at each thread to avoid both contention and slow accesses to memory banks attached to other sockets. Experiments show that such exploitation reduces per-iteration time by 33–98%, for three real ML workloads, and that these improvements are robust to variation in the patterns over time.