SEP-Graph: Finding Shortest Execution Paths for Graph Processing under a Hybrid Framework on GPU
In general, the performance of parallel graph processing is determined by three pairs of critical parameters, namely synchronous or asynchronous execution mode (Sync or Async), Push or Pull communication mechanism (Push or Pull), and Data-driven or Topology-driven traversing scheme (DD or TD), which increases the complexity and sophistication of programming and system implementation of GPU. Existing graph-processing frameworks mainly use a single combination in the entire execution for a given application, but we have observed their variable and suboptimal performance.
In this paper, we present SEP-Graph, a highly efficient software framework for graph-processing on GPU. The hybrid execution mode is automatically switched among three pairs of parameters, with an objective to achieve the shortest execution time in each iteration. We also apply a set of optimizations to SEP-Graph, considering the characteristics of graph algorithms and underlying GPU architectures. We show the effectiveness of SEP-Graph based on our intensive and comparative performance evaluation on NVIDIA 1080, P100, and V100 GPUs. Compared with existing and representative GPU graph-processing framework Groute and Gunrock, SEP-Graph can reduce execution time up to 45.8 times and 39.4 times.
Mon 18 FebDisplayed time zone: Guadalajara, Mexico City, Monterrey change
10:55 - 12:35 | Session 2: Heterogeneous Platforms and GPUMain Conference at Salon 12/13 Chair(s): Xu Liu College of William and Mary | ||
10:55 25mTalk | Throughput-Oriented GPU Memory Allocation Main Conference DOI | ||
11:20 25mTalk | SEP-Graph: Finding Shortest Execution Paths for Graph Processing under a Hybrid Framework on GPU Main Conference Hao Wang The Ohio State University, USA, Liang Geng The Ohio State University, USA, Rubao Lee United Parallel Computing Corporation, USA, Kaixi Hou Virginia Tech, USA, Yanfeng Zhang , Xiaodong Zhang The Ohio State University, USA DOI | ||
11:45 25mTalk | Incremental Flattening for Nested Data Parallelism Main Conference Troels Henriksen University of Copenhagen, Denmark, Frederik Thorøe DIKU, University of Copenhagen, Martin Elsman University of Copenhagen, Denmark, Cosmin Oancea University of Copenhagen, Denmark DOI | ||
12:10 25mTalk | Adaptive Sparse Matrix-Matrix Multiplication on the GPU Main Conference Martin Winter Graz University of Technology, Austria, Daniel Mlakar Graz University of Technology, Austria, Rhaleb Zayer Max Planck Institute for Informatics, Hans-Peter Seidel Max Planck Institute for Informatics, Markus Steinberger Graz University of Technology, Austria DOI | ||