Supercomputing of circuits simulation
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
An efficient algorithm for sparse matrix computations
SAC '92 Proceedings of the 1992 ACM/SIGAPP symposium on Applied computing: technological challenges of the 1990's
A high performance algorithm using pre-processing for the sparse matrix-vector multiplication
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Matrix market: a web resource for test matrix collections
Proceedings of the IFIP TC2/WG2.5 working conference on Quality of numerical software: assessment and enhancement
Improving the memory-system performance of sparse-matrix vector multiplication
IBM Journal of Research and Development
Compressed inverted files with reduced decoding overheads
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Symbolic Generation of an Optimal Crout Algorithm for Sparse Systems of Linear Equations
Journal of the ACM (JACM)
Improving performance of sparse matrix-vector multiplication
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
Compression of inverted indexes For fast query evaluation
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Compact representations of separable graphs
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Inverted file compression through document identifier reassignment
Information Processing and Management: an International Journal
Index Compression through Document Reordering
DCC '02 Proceedings of the Data Compression Conference
Towards Compressing Web Graphs
DCC '01 Proceedings of the Data Compression Conference
Compressing the Graph Structure of the Web
DCC '01 Proceedings of the Data Compression Conference
On Improving the Performance of Sparse Matrix-Vector Multiplication
HIPC '97 Proceedings of the Fourth International Conference on High-Performance Computing
Assigning document identifiers to enhance compressibility of Web Search Engines indexes
Proceedings of the 2004 ACM symposium on Applied computing
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Sparsity: Optimization Framework for Sparse Matrix Kernels
International Journal of High Performance Computing Applications
Optimizing Sparse Matrix-Vector Product Computations Using Unroll and Jam
International Journal of High Performance Computing Applications
Fast sparse matrix-vector multiplication by exploiting variable block structure
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
Vectorized sparse matrix multiply for compressed row storage format
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
An operation stacking framework for large ensemble computations
Proceedings of the 21st annual international conference on Supercomputing
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
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Performance evaluation of the sparse matrix-vector multiplication on modern architectures
The Journal of Supercomputing
Operation Stacking for Ensemble Computations With Variable Convergence
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)
Hierarchical Diagonal Blocking and Precision Reduction Applied to Combinatorial Multigrid
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
CSX: an extended compression format for spmv on shared memory systems
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Exploiting dense substructures for fast sparse matrix vector multiplication
International Journal of High Performance Computing Applications
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
In-memory data compression for sparse matrices
IA^3 '13 Proceedings of the 3rd Workshop on Irregular Applications: Architectures and Algorithms
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Sparse matrix computations are important for many scientific computations, with matrix-vector multiplication being a fundamental operation for modern iterative algorithms. For large sparse matrices, the primary performance limitation on matrix-vector product is memory bandwidth, rather than algorithm performance. In fact, the wide disparity between memory bandwidth and CPU performance suggests that one could trade cycles for bandwidth and still improve the time to compute a matrix-vector product. Accordingly, this paper presents an approach to improving the performance of matrix-vector product based on lossless compression of the index information commonly stored in sparse matrix representations. Two compressed formats, and their multiplication algorithms, are given, along with experimental results demonstrating their effectiveness. For an assortment of large sparse matrices, compression ratios and corresponding speedups of up to 30% are achieved. The efficiency of the compression algorithm allows its cost to be easily amortized across repeated matrix-vector products.