CSR5：一种适用于跨平台稀疏矩阵-向量乘法的高效存储格式
CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication
AI总结：
本文提出CSR5存储格式，通过低开销转换实现跨平台高效稀疏矩阵-向量乘法，对规则和不规则矩阵均有效，并在多个平台上取得显著性能提升。
摘要：
稀疏矩阵-向量乘法（SpMV）是许多应用的基础构建块。本文提出CSR5（压缩稀疏行5）存储格式，可在各种平台（包括CPU、GPU和Xeon Phi）上实现高吞吐量的SpMV。首先，CSR5格式对输入矩阵的稀疏性结构不敏感，因此单一格式可支持既高效处理规则矩阵又高效处理不规则矩阵的SpMV算法。此外，我们展示了从CSR到CSR5的格式转换开销可低至几次SpMV操作的成本。我们比较了基于CSR5的SpMV算法与11种最先进的格式和算法，在四个主流处理器上使用14个规则和10个不规则矩阵作为基准套件。对于套件中的14个规则矩阵，我们实现了与先前工作相当或更好的性能。对于10个不规则矩阵，CSR5在双路Intel CPU、nVidia GPU、AMD GPU和Intel Xeon Phi上分别取得平均性能提升17.6%、28.5%、173.0%和293.3%（最高213.3%、153.6%、405.1%和943.3%）。对于仅需要数十次迭代的现实应用，CSR5因其低开销的格式转换而更具实用性。该工作的源代码可在https://github.com/bhSPARSE/Benchmark_SpMV_using_CSR5下载。
英文摘要：
Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi. First, the CSR5 format is insensitive to the sparsity structure of the input matrix. Thus the single format can support an SpMV algorithm that is efficient both for regular matrices and for irregular matrices. Furthermore, we show that the overhead of the format conversion from the CSR to the CSR5 can be as low as the cost of a few SpMV operations. We compare the CSR5-based SpMV algorithm with 11 state-of-the-art formats and algorithms on four mainstream processors using 14 regular and 10 irregular matrices as a benchmark suite. For the 14 regular matrices in the suite, we achieve comparable or better performance over the previous work. For the 10 irregular matrices, the CSR5 obtains average performance improvement of 17.6\%, 28.5\%, 173.0\% and 293.3\% (up to 213.3\%, 153.6\%, 405.1\% and 943.3\%) over the best existing work on dual-socket Intel CPUs, an nVidia GPU, an AMD GPU and an Intel Xeon Phi, respectively. For real-world applications such as a solver with only tens of iterations, the CSR5 format can be more practical because of its low-overhead for format conversion. The source code of this work is downloadable at https://github.com/bhSPARSE/Benchmark_SpMV_using_CSR5
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
