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.
Extracting Useful Computation From Error-Prone Processors For Streaming Applications
Design, Automation & Test in Europe Conference (DATE'13), March 2013.
Yavuz Yetim, Margaret Martonosi, and Sharad Malik
As semiconductor fabrics scale closer to fundamental physical limits, their reliability is decreasing due to process variation, noise margin effects, aging effects, and increased susceptibility to soft errors. Reliability can be regained through redundancy, error checking with recovery, voltage scaling and other means, but these techniques impose area/energy costs. Since some applications (e.g. media) can tolerate limited computation errors and still provide useful results, error-tolerant computation models have been explored, with both the application and computation fabric having stochastic characteristics. Stochastic computation has, however, largely focused on application-specific hardware solutions, and is not general enough to handle arbitrary bit errors that impact memory addressing or control in processors.
In response, this paper addresses requirements for errortolerant execution by proposing and evaluating techniques for running error-tolerant software on a general-purpose processor built from an unreliable fabric. We study the minimum errorprotection required, from a microarchitecture perspective, to still produce useful results at the application output. Even with random errors as frequent as every 250μs, our proposed design allows JPEG and MP3 benchmarks to sustain good output quality—14dB and 7dB respectively. Overall, this work establishes the potential for error-tolerant single-threaded execution, and details its required hardware/system support.
FULL PAPER: pdf