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Massively Parallel Graph processing on GPUs

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MapGraph is Massively Parallel Graph processing on GPUs.

- 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 strategies, such as vertex-degree-dependent dynamic parallelism granularity and frontier compaction.

- MapGraph is up to two orders of magnitude faster than parallel CPU implementations on up 24 CPU cores and has performance comparable to a state-of-the-art manually optimized GPU implementation.

- 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 3.3B traversed edges per second on a single GPU.

This work was (partially) funded by the DARPA XDATA program under AFRL Contract #FA8750-13-C-0002 and DARPA Contract #D14PC00029.

mapgraph Web Site


  • GPU
  • Graph Mining


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Government, Science/Research, Developers

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