Optimization for sparse matrix-vector multiplication based on NVIDIA CUDA platform

• Main Authors: Tsai,Sung-Han, 蔡松翰
• Other Authors: Wei, Kai-Cheng
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/qw23p7

碩士 === 國立彰化師範大學 === 資訊工程學系 === 105 === In recent years, large size sparse matrices are often used in fields such as science and engineering which usually apply in computing linear model. Using the ELLPACK format to store sparse matrices, it can reduce the matrix storage space. But if there is too much nonzero elements in one of row of the original sparse matrix, it still waste too much memory space. There are many research focusing on the Sparse Matrix–Vector Multiplication（SpMV）with ELLPACK format on Graphic Processing Unit（GPU）. Therefore, the purpose of our research is reducing the access space of sparse matrix which is transformed in Compressed Sparse Row（CSR）format after Reverse Cutthill-McKee（RCM）algorithm to accelerate for SpMV on GPU. Due to lower data access ratio from SpMV, the performance is restricted by memory bandwidth. Our propose is based on CSR format from two aspects:（1）reduce cache misses to enhance the vector locality and raise the performance, and（2）reduce accessed matrix data by index reduction to optimize the performance.