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Dinkins, S. (2012) A Model for Predicting the Performance of Sparse Matrix Vector Multiply (SpMV) Using Memory Bandwidth Requirements and Data Locality. Master’s Thesis, Colorado State University, Fort Collins.

has been cited by the following article:

• TITLE: Performance Prediction Based on Statistics of Sparse Matrix-Vector Multiplication on GPUs

  AUTHORS: Ruixing Wang, Tongxiang Gu, Ming Li

  KEYWORDS: Sparse Matrix-Vector Multiplication, Performance Prediction, GPU, Normal Distribution, Uniform Distribution

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.6, April 28, 2017

  ABSTRACT: As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo and Wang put forward a new idea to predict the performance of SpMV on GPUs. However, they didn’t consider the matrix structure completely, so the execution time predicted by their model tends to be inaccurate for general sparse matrix. To address this problem, we proposed two new similar models, which take into account the structure of the matrices and make the performance prediction model more accurate. In addition, we predict the execution time of SpMV for CSR-V, CSR-S, ELL and JAD sparse matrix storage formats by the new models on the CUDA platform. Our experimental results show that the accuracy of prediction by our models is 1.69 times better than Guo and Wang’s model on average for most general matrices.