The Comparison of Large-Scale Graph Processing Algorithms Implementation Methods for Intel KNL and NVIDIA GPU

Abstract

The paper describes implementation approaches to large-graph processing on two modern high-performance computational platforms: NVIDIA GPU and Intel KNL. The described approach is based on a deep a priori analysis of algorithm properties that helps to choose implementation method correctly. To demonstrate the proposed approach, shortest paths and strongly connected components computation problems have been solved for sparse graphs. The results include detailed description of the whole algorithm’s development cycle: from algorithm information structure research and selection of efficient implementation methods, suitable for the particular platforms, to specific optimizations for each of the architectures. Based on the joint analysis of algorithm properties and architecture features, a performance tuning, including graph storage format optimizations, efficient usage of the memory hierarchy and vectorization is performed. The developed implementations demonstrate high performance and good scalability of the proposed solutions. In addition, a lot of attention was paid to profiling implemented algorithms with NVIDIA Visual Profiler and Intel® VTune ™ Amplifier utilities. This allows current paper to present a fair comparison, demonstrating advantages and disadvantages of each platform for large-scale graph processing.