MPGraphv3 Release

We are please to announce the v3 release of MPGraph. The MPGraph API makes it easy to develop high performance graph analytics on GPUs. The API is based on the Gather-Apply-Scatter (GAS) model as used in GraphLab. To deliver high performance computation and efficiently utilize the high memory bandwidth of GPUs, MPGraph’s CUDA kernels use multiple sophisticated strategies, such as vertex-degree-dependent dynamic parallelism granularity and frontier compaction.

The v3 release includes a 5x – 10x performance gain in algorithms that have large frontiers (Connected Components, Page Rank, etc.). This performance gain is obtained by using a different strategy to load balance the GPU when the frontier is large. This strategy has more overhead for small frontiers, but outperforms the existing kernels when the frontier becomes large.  MPGraph automatically chooses the best strategy for each iteration of the computation.

Download MPGraph v3<http://sourceforge.net/projects/mpgraph/files/latest/download?source=files> from SourceForge now. Or you can get the latest development version from SVN:

svn checkout svn://svn.code.sf.net/p/mpgraph/code/trunk

You can learn more about MPGraph at GTC next week<http://registration.gputechconf.com/quicklink/b1cyGlI>.  We will be presenting on Monday the 24th in San Jose.

The goal of this session<http://registration.gputechconf.com/quicklink/fo9qLPo> is to demonstrate how our high level abstraction enables developers to quickly develop high performance graph analytics programs on GPUs with up to 3 billion edges traversed per second on a Tesla or Kepler GPU. High performance graph analytics are critical for a large range of application domains. The SIMT architecture of the GPUs and the irregularity nature of the graphs make it difficult to develop efficient graph analytics programs. In this session, we present an open source library that provides a high level abstraction for efficient graph analytics with minimal coding effort. We use several specific examples to show how to use our abstraction to implement efficient graph analytics in a matter of hours.

We will be presenting new results for MPGraph v3<http://sourceforge.net/projects/mpgraph/>.  These results include significant speedups for problems with very large frontiers.

For more information about the GPU Technology Conference, see http://www.gputechconf.com/page/home.html.  For more information about the MPGraph presentation, see http://registration.gputechconf.com/quicklink/b1cyGlI.  For more information about MPGraph, see http://sourceforge.net/projects/mpgraph/  and http://www.systap.com/mpgraph/api/html/index.html.

Thanks,

Bryan Thompson