Performance Optimization for SpMV on Multi-GPU Systems Using Threads and Multiple Streams

Ping Guo, Changjiang Zhang
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引用次数: 4

Abstract

Sparse matrix-vector multiplication (SpMV) is a key operation in scientific computing and engineering ap-plications. This paper presents an optimization strategy to improve SpMV performance on the multi-GPU systems by adopting OpenMP threads and multiple CUDA streams. We propose an efficient scheme to control multiple GPUs jointly complete SpMV computations by making use of OpenMP threads. Moreover, we adopt streamed approach to increase concurrency to further improve SpMV performance. In our paper, we use HYB (Hybrid ELL/COO), a hybrid sparse storage format, to demonstrate the effectiveness of our proposed approach. Our experimental results show that our approach achieves an average speedup of 3.80 over the existing SpMV implementation on a single GPU.
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基于多线程和多流的多gpu系统SpMV性能优化
稀疏矩阵向量乘法(SpMV)是科学计算和工程应用中的关键运算。本文提出了一种在多gpu系统上采用OpenMP线程和多CUDA流来提高SpMV性能的优化策略。我们提出了一种利用OpenMP线程控制多个gpu共同完成SpMV计算的有效方案。此外,我们采用流化的方法来提高并发性,进一步提高SpMV的性能。在本文中,我们使用混合稀疏存储格式HYB (Hybrid ELL/COO)来证明我们提出的方法的有效性。我们的实验结果表明,我们的方法比现有的SpMV实现在单个GPU上的平均加速提高了3.80。
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