scholarly journals High-Level Strategies for Parallel Shared-Memory Sparse Matrix-Vector Multiplication

2014 ◽  
Vol 25 (1) ◽  
pp. 116-125 ◽  
Author(s):  
Albert-Jan Nicholas Yzelman ◽  
Dirk Roose
Keyword(s):  
Shared Memory ◽  
Sparse Matrix ◽  
Matrix Vector Multiplication ◽  
High Level ◽  
Matrix Vector

scholarly journals Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jiaquan Gao ◽  
Panpan Qi ◽  
Guixia He
Keyword(s):  
Iterative Methods ◽  
Shared Memory ◽  
Eigenvalue Problems ◽  
Sparse Matrix ◽  
Computational Science ◽  
Test Results ◽  
Thread Block ◽  
Matrix Vector Multiplication ◽  
Compressed Sparse Row ◽  
Matrix Vector

Sparse matrix-vector multiplication (SpMV) is an important operation in computational science and needs be accelerated because it often represents the dominant cost in many widely used iterative methods and eigenvalue problems. We achieve this objective by proposing a novel SpMV algorithm based on the compressed sparse row (CSR) on the GPU. Our method dynamically assigns different numbers of rows to each thread block and executes different optimization implementations on the basis of the number of rows it involves for each block. The process of accesses to the CSR arrays is fully coalesced, and the GPU’s DRAM bandwidth is efficiently utilized by loading data into the shared memory, which alleviates the bottleneck of many existing CSR-based algorithms (i.e., CSR-scalar and CSR-vector). Test results on C2050 and K20c GPUs show that our method outperforms a perfect-CSR algorithm that inspires our work, the vendor tuned CUSPARSE V6.5 and CUSP V0.5.1, and three popular algorithms clSpMV, CSR5, and CSR-Adaptive.

scholarly journals Sparse Matrix-Vector Multiplication on GPGPUs

2017 ◽  
Vol 43 (4) ◽  
pp. 1-49 ◽  
Author(s):  
Salvatore Filippone ◽  
Valeria Cardellini ◽  
Davide Barbieri ◽  
Alessandro Fanfarillo
Keyword(s):  
Sparse Matrix ◽  
Matrix Vector Multiplication ◽  
Matrix Vector
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