From the post:
MapGraph is Massively Parallel Graph processing on GPUs. (Previously known as “MPGraph”).
- The MapGraph 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, MapGraph’s CUDA kernels use multiple sophisticated strategies, such as vertex-degree-dependent dynamic parallelism granularity and frontier compaction.
- New algorithms can be implemented in a few hours that fully exploit the data-level parallelism of the GPU and offer throughput of up to 3 billion traversed edges per second on a single GPU.
- Preliminary results for the multi-GPU version of MapGraph have traversal rates of nearly 30 GTEPS (30 billion traversed edges per second) on a scale-free random graph with 4.3 billion directed edges using a 64 GPU cluster. See the multi-GPU paper referenced below for details.
- The MapGraph API also comes in a CPU-only version that is currently packaged and distributed with the bigdata open-source graph database. GAS programs operate over the graph data loaded into the database and are accessed via either a Java API or a SPARQL 1.1 Service Call . Packaging the GPU version inside bigdata will be in a future release.
MapGraph is under the Apache 2 license. You can download MapGraph from http://sourceforge.net/projects/mpgraph/ . For the lastest version of this documentation, see http://mapgraph.io. You can subscribe to receive notice for future updates on the project home page. For open source support, please ask a question on the MapGraph mailing lists or file a ticket. To inquire about commercial support, please email us at firstname.lastname@example.org. You can follow MapGraph and the bigdata graph database platform at http://www.bigdata.com/blog.
This work was (partially) funded by the DARPA XDATA program under AFRL
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under
Contract No. D14PC00029.
- MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs – Zhisong Fu, Bryan Thompson, Michael Personick (SYSTAP, LLC). GRADES 2014, June 22, 2014, Snowbird, Utah, USA. Copyright 2014 ACM 978-1-4503-2982-8. DOI:http://dx.doi.org/10.1145/2621934.2621936
- Z. Fu, H.K. Dasari, B. Bebee, M. Berzins, B. Thompson. Parallel Breadth First Search on GPU Clusters. IEEE Big Data. Bethesda, MD. 2014.
- Parallel Breadth First Search on GPU Clusters – Z. Fu, H.K. Dasari, M. Berzins, B. Thompson. SCI Technical Report, SCI Institute, University of Utah. July 29, 2014. URL:http://www.sci.utah.edu/publications/Fu2014a/UUSCI-2014-002.pdf
You do have to wonder when the folks at Systap sleep. 😉 This is the same group that produced BlazeGraph, recently adopted by WikiData. Granting WikiData only has 13.6 million data items as of today but it isn’t “small” data.
The rest of the page has additional pointers and explanations for MapGraph.