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Despite numerous efforts for optimizing the performance of Sparse Matrix andVector Multiplication (SpMV) on modern hardware architectures, few works aredone to its sparse counterpart, Sparse Matrix and Sparse Vector Multiplication(SpMSpV), not to mention dealing with input vectors of varied sparsity. The keychallenge is that depending on the sparsity levels, distribution of data, andcompute platform, the optimal choice of SpMV/SpMSpV kernel can vary, and astatic choice does not suffice. In this paper, we propose an adaptiveSpMV/SpMSpV framework, which can automatically select the appropriateSpMV/SpMSpV kernel on GPUs for any sparse matrix and vector at the runtime.Based on systematic analysis on key factors such as computing pattern, workloaddistribution and write-back strategy, eight candidate SpMV/SpMSpV kernels areencapsulated into the framework to achieve high performance in a seamlessmanner. A comprehensive study on machine learning based kernel selector isperformed to choose the kernel and adapt with the varieties of both the inputand hardware from both accuracy and overhead perspectives. Experimentsdemonstrate that the adaptive framework can substantially outperform theprevious state-of-the-art in real-world applications on NVIDIA Tesla K40m, P100and V100 GPUs.

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity