Importance of explicit vectorization for CPU and GPU software performance

JOURNAL OF COMPUTATIONAL PHYSICS

卷:230

Article

Dickson, Neil G.¹  Karimi, Kamran¹  Hamze, Firas¹ 

[1] D Wave Syst Inc, Burnaby, BC V5C 6G9, Canada

关键词: Performance;    Optimization;    Vectorization;    Monte Carlo;    Ising model;    GPU;   

DOI  :  10.1016/j.jcp.2011.03.041

来源: Elsevier

【 摘 要 】

Much of the current focus in high-performance computing is on multi-threading, multi-computing, and graphics processing unit (CPU) computing. However, vectorization and non-parallel optimization techniques, which can often be employed additionally, are less frequently discussed. In this paper, we present an analysis of several optimizations done on both central processing unit (CPU) and CPU implementations of a particular computationally intensive Metropolis Monte Carlo algorithm. Explicit vectorization on the CPU and the equivalent, explicit memory coalescing, on the CPU are found to be critical to achieving good performance of this algorithm in both environments. The fully-optimized CPU version achieves a 9x to 12x speedup over the original CPU version, in addition to speedup from multi-threading. This is 2x faster than the fully-optimized CPU version, indicating the importance of optimizing CPU implementations. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

Free