Abstract: Evaluating Topology Mapping via Graph Partitioning
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis（2012）
摘要
Intelligently mapping applications to machine network topologies has been shown to improve performance, but considerable developer effort is required to find good mappings. Techniques from graph partitioning have the potential to automate topology mapping and relieve the developer burden. Graph partitioning is already used for load balancing parallel applications, but can be applied to topology mapping as well. We show performance gains by using a topology-targeting graph partitioner to map sparse matrix-vector and volumetric 3-D FFT kernels onto a 3-D torus network.
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关键词
topology mapping,good mapping,sparse matrix-vector,considerable developer effort,performance gain,topology mapping evaluation,matrix algebra,network topology,3-d torus network,volumetric 3-d fft kernel,developer burden,topology-targeting graph partitioner,parallel applications,graph theory,machine network topology,machine network topologies,graph partitioning,intelligently mapping applications,load balancing,volumetric 3-d fft kernels,fast fourier transforms
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