Optimizing Sparse Matrix Computations for Register Reuse in SPARSITY
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Performance optimizations and bounds for sparse matrix-vector multiply
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Optimizing the performance of sparse matrix-vector multiplication
Optimizing the performance of sparse matrix-vector multiplication
Automatic performance tuning of sparse matrix kernels
Automatic performance tuning of sparse matrix kernels
Accelerating sparse matrix computations via data compression
Proceedings of the 20th annual international conference on Supercomputing
When cache blocking of sparse matrix vector multiply works and why
Applicable Algebra in Engineering, Communication and Computing
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Optimizing sparse matrix-vector multiplication using index and value compression
Proceedings of the 5th conference on Computing frontiers
Pattern-based sparse matrix representation for memory-efficient SMVM kernels
Proceedings of the 23rd international conference on Supercomputing
Implementing sparse matrix-vector multiplication on throughput-oriented processors
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Fast sparse matrix-vector multiplication by exploiting variable block structure
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
SMAT: an input adaptive auto-tuner for sparse matrix-vector multiplication
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
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Sparse Matrix-Vector multiplication (SpMV) is an important computational kernel in scientific applications. Its performance highly depends on the nonzero distribution of sparse matrices. In this paper, we propose a new storage format for diagonal sparse matrices, defined as Compressed Row Segment with Diagonal-pattern (CRSD). We design diagonal patterns to represent the diagonal distribution. As the diagonal distributions are similar within matrices from one application, some diagonal patterns remain unchanged. First, we sample one matrix to obtain the unchanged diagonal patterns. Next, the optimal SpMV codelets are generated automatically for those diagonal patterns. Finally, we combine the generated codelets as the optimal SpMV implementation. In addition, the information collected during auto-tuning process is also utilized for parallel implementation to achieve load-balance. Experimental results demonstrate that the speedup reaches up to 2.37 (1.70 on average) in comparison with DIA and 4.60 (2.10 on average) in comparison with CSR under the same number of threads on two mainstream multi-core platforms.