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.
Computing Infrastructure for Big Data Processing
Frontiers of Computer Science, 2013.
Georgia Institute of Technology
With computing systems transforming from single-processor devices to the ubiquitous and networked devices and the datacenter-scale computing in the cloud, the parallelism has become ubiquitous at many levels. At micro level, parallelisms are being explored from the underlying circuits, to pipelining and instruction level parallelism on multi-cores or many cores on a chip as well as in a machine. From macro level, parallelisms are being promoted from multiple machines on a rack, many racks in a data center, to the globally shared infrastructure of the Internet. With the push of big data, we are entering a new era of parallel computing driven by novel and ground breaking research innovation on elastic parallelism and scalability. In this article, we will give an overview of computing infrastructure for big data processing, focusing on architectural, storage and networking challenges of supporting big data analysis. We will briefly discuss emerging computing infrastructure and technologies that are promising for improving data parallelism, task parallelism and encouraging vertical and horizontal computation parallelism.
FULL PAPER: pdf