Implementation and Optimization of miniGMG - a Compact Geometric Multigrid Benchmark

Williams, Samuel ; Kalamkar, Dhiraj ; Singh, Amik ; Deshpande, Anand M. ; Straalen, Brian Van ; Smelyanskiy, Mikhail ; Almgren, Ann ; Dubey, Pradeep ; Shalf, John ; Oliker, Leonid

关键词: Multigrid;    Xeon Phi;    Knights Corner;    GPU;    Optimization;   

DOI  :  10.2172/1136783
RP-ID  :  LBNL-6676E
PID  :  OSTI ID: 1136783

学科分类：数学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

Multigrid methods are widely used to accelerate the convergence of iterative solvers for linear systems used in a number of different application areas. In this report, we describe miniGMG, our compact geometric multigrid benchmark designed to proxy the multigrid solves found in AMR applications. We explore optimization techniques for geometric multigrid on existing and emerging multicore systems including the Opteron-based Cray XE6, Intel Sandy Bridge and Nehalem-based Infiniband clusters, as well as manycore-based architectures including NVIDIA's Fermi and Kepler GPUs and Intel's Knights Corner (KNC) co-processor. This report examines a variety of novel techniques including communication-aggregation, threaded wavefront-based DRAM communication-avoiding, dynamic threading decisions, SIMDization, and fusion of operators. We quantify performance through each phase of the V-cycle for both single-node and distributed-memory experiments and provide detailed analysis for each class of optimization. Results show our optimizations yield significant speedups across a variety of subdomain sizes while simultaneously demonstrating the potential of multi- and manycore processors to dramatically accelerate single-node performance. However, our analysis also indicates that improvements in networks and communication will be essential to reap the potential of manycore processors in large-scale multigrid calculations.