Improving the memory-system performance of sparse-matrix vector multiplication
IBM Journal of Research and Development
Improving performance of sparse matrix-vector multiplication
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Achieving high sustained performance in an unstructured mesh CFD application
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
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
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Optimizing the performance of sparse matrix-vector multiplication
Optimizing the performance of sparse matrix-vector multiplication
On Improving the Performance of Sparse Matrix-Vector Multiplication
HIPC '97 Proceedings of the Fourth International Conference on High-Performance Computing
Performance Models for Evaluation and Automatic Tuning of Symmetric Sparse Matrix-Vector Multiply
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
Sparse matrix storage revisited
Proceedings of the 2nd conference on Computing frontiers
Optimizing Sparse Matrix-Vector Product Computations Using Unroll and Jam
International Journal of High Performance Computing Applications
Accelerating sparse matrix computations via data compression
Proceedings of the 20th annual international conference on Supercomputing
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
High Throughput Compression of Double-Precision Floating-Point Data
DCC '07 Proceedings of the 2007 Data Compression Conference
Understanding the Performance of Sparse Matrix-Vector Multiplication
PDP '08 Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008)
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Fast sparse matrix-vector multiplication by exploiting variable block structure
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
Pattern-based sparse matrix representation for memory-efficient SMVM kernels
Proceedings of the 23rd international conference on Supercomputing
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Operation Stacking for Ensemble Computations With Variable Convergence
International Journal of High Performance Computing Applications
International Journal of High Performance Computing Applications
A compiler-automated array compression scheme for optimizing memory intensive programs
Proceedings of the 24th ACM International Conference on Supercomputing
Exploiting compression opportunities to improve SpMxV performance on shared memory systems
ACM Transactions on Architecture and Code Optimization (TACO)
CSX: an extended compression format for spmv on shared memory systems
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Proceedings of the 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era
CRSD: application specific auto-tuning of SpMV for diagonal sparse matrices
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
Sparse matrix-vector multiply on the HICAMP architecture
Proceedings of the 26th ACM international conference on Supercomputing
Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform
The Journal of Supercomputing
Accelerating sparse matrix-vector multiplication on GPUs using bit-representation-optimized schemes
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Sparse matrix-vector multiplication on the Single-Chip Cloud Computer many-core processor
Journal of Parallel and Distributed Computing
| Hi-index | 0.00 |
Previous research work has identified memory bandwidth as the main bottleneck of the ubiquitous Sparse Matrix-Vector Multiplication kernel. To attack this problem, we aim at reducing the overall data volume of the algorithm. Typical sparse matrix representation schemes store only the non-zero elements of the matrix and employ additional indexing information to properly iterate over these elements. In this paper we propose two distinct compression methods targeting index and numerical values respectively. We perform a set of experiments on a large real-world matrix set and demonstrate that the index compression method can be applied successfully to a wide range of matrices. Moreover, the value compression method is able to achieve impressive speedups in a more limited yet important class of sparse matrices that contain a small number of distinct values