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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity
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Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

机译：适应性SPMV / SPMSPV在GPU上进行各种稀疏性的输入向量

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摘要

Despite numerous efforts for optimizing the performance of Sparse Matrix and Vector Multiplication (SpMV) on modern hardware architectures, few works are done to its sparse counterpart, Sparse Matrix and Sparse Vector Multiplication (SpMSpV), not to mention dealing with input vectors of varied sparsity. The key challenge is that depending on the sparsity levels, distribution of data, and compute platform, the optimal choice of SpMV/SpMSpV kernel can vary, and a static choice does not suffice. In this article, we propose an adaptive SpMV/SpMSpV framework, which can automatically select the appropriate SpMV/SpMSpV kernel on GPUs for any sparse matrix and vector at the runtime. Based on systematic analysis on key factors such as computing pattern, workload distribution and write-back strategy, eight candidate SpMV/SpMSpV kernels are encapsulated into the framework to achieve high performance in a seamless manner. A comprehensive study on machine learning-based kernel selector is performed to choose the kernel and adapt with the varieties of both the input and hardware from both accuracy and overhead perspectives. Experiments demonstrate that the adaptive framework can substantially outperform the previous state-of-the-art in real-world applications on NVIDIA Tesla K40m, P100, and V100 GPUs.
机译：尽管有许多努力优化现代硬件架构上的稀疏矩阵和矢量乘法（SPMV）的努力，但很少有效地在其稀疏的对应物，稀疏矩阵和稀疏向量乘法（SPMSPV）上完成，更不用说处理各种稀疏性的输入向量。关键挑战是，根据稀疏性级别，数据分布和计算平台，SPMV / SPMSPV内核的最佳选择可以变化，并且静态选择不足。在本文中，我们提出了一种自适应SPMV / SPMSPV框架，它可以在GPU上自动选择适当的SPMV / SPMSPV内核，用于运行时的任何稀疏矩阵和向量。基于对计算模式，工作量分配和回写策略等关键因素的系统分析，八个候选SPMV / SPMSPV内核封装到框架中以实现高性能以无缝方式实现高性能。对基于机器学习的内核选择器进行了全面的研究，以选择内核，并从精度和架空视角下使用输入和硬件的各种品种。实验表明，自适应框架可以在NVIDIA Tesla K40M，P100和V100GPU上的现实世界应用中显着优于先前的现实应用。

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