Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer
Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer
复制标题
神威·太湖之光超级计算机上稀疏矩阵-矩阵乘法的性能感知模型
DOI：
10.1109/tpds.2018.2871189
复制
发表时间：
2019-04-01
影响因子：
5.3
通讯作者：
Li, Tao
中科院分区：
文献类型：
--
作者：
Chen, Yuedan;Li, Kenli;Li, Tao
一般稀疏矩阵-稀疏矩阵乘法（SpGEMM）是各种科学应用中的基本线性运算之一。为了在大规模应用中实现高效的SpGEMM，本文在神威太湖之光超级计算机上提出了基于COO、CSR、ELL和CSC格式的可扩展优化SpGEMM内核。首先，SpGEMM的多级并行设计，提出了利用超过10万个核心的并行性和更好地控制基于特殊的Sunway架构的存储器。采用负载平衡、合并DMA传输、数据重用、向量化计算和并行流水线处理等优化策略，进一步优化了SpGEMM内核的性能。其次，我们彻底分析了所提出的内核的性能。第三，提出了一个性能感知的SpGEMM模型，以选择最合适的压缩存储格式的稀疏矩阵，可以实现最佳性能的SpGEMM上的Sunway。实验结果表明，SpGEMM内核具有良好的可扩展性，能够满足Sunway上大规模数据集高速计算的要求。此外，SpGEMM的性能感知模型在单个进程中执行内核时平均相对错误率的绝对值为8.31%，在多个进程中执行内核时平均相对错误率的绝对值为8.59%。实验结果表明，该模型具有较高的精度，能够满足在神威太湖之光超级计算机上进行SpGEMM格式选择的精度要求。
General sparse matrix-sparse matrix multiplication (SpGEMM) is one of the fundamental linear operations in a wide variety of scientific applications. To implement efficient SpGEMM for many large-scale applications, this paper proposes scalable and optimized SpGEMM kernels based on COO, CSR, ELL, and CSC formats on the Sunway TaihuLight supercomputer. First, a multi-level parallelism design for SpGEMM is proposed to exploit the parallelism of over 10 millions cores and better control memory based on the special Sunway architecture. Optimization strategies, such as load balance, coalesced DMA transmission, data reuse, vectorized computation, and parallel pipeline processing, are applied to further optimize performance of SpGEMM kernels. Second, we thoroughly analyze the performance of the proposed kernels. Third, a performance-aware model for SpGEMM is proposed to select the most appropriate compressed storage formats for the sparse matrices that can achieve the optimal performance of SpGEMM on the Sunway. The experimental results show the SpGEMM kernels have good scalability and meet the challenge of the high-speed computing of large-scale data sets on the Sunway. In addition, the performance-aware model for SpGEMM achieves an absolute value of relative error rate of 8.31 percent on average when the kernels are executed in one single process and achieves 8.59 percent on average when the kernels are executed in multiple processes. It is proved that the proposed performance-aware model can perform at high accuracy and satisfies the precision of selecting the best formats for SpGEMM on the Sunway TaihuLight supercomputer.