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
Characterizing the behavior of sparse algorithms on caches
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Compilation techniques for sparse matrix computations
ICS '93 Proceedings of the 7th international conference on Supercomputing
Hitting the memory wall: implications of the obvious
ACM SIGARCH Computer Architecture News
Improving the memory-system performance of sparse-matrix vector multiplication
IBM Journal of Research and Development
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
Towards a fast parallel sparse symmetric matrix-vector multiplication
Parallel Computing - Linear systems and associated problems
Optimizing Sparse Matrix Computations for Register Reuse in SPARSITY
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors
Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors
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
Automatic performance tuning of sparse matrix kernels
Automatic performance tuning of sparse matrix kernels
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
Accelerating sparse matrix computations via data compression
Proceedings of the 20th annual international conference on Supercomputing
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
Optimizing sparse matrix-vector multiplication using index and value compression
Proceedings of the 5th conference on Computing frontiers
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
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
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
Optimization of sparse matrix-vector multiplication using reordering techniques on GPUs
Microprocessors & Microsystems
Sparse matrix-vector multiply on the HICAMP architecture
Proceedings of the 26th ACM international conference on 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
Power system probabilistic and security analysis on commodity high performance computing systems
HiPCNA-PG '13 Proceedings of the 3rd International Workshop on High Performance Computing, Networking and Analytics for the Power Grid
Sparse matrix-vector multiplication on the Single-Chip Cloud Computer many-core processor
Journal of Parallel and Distributed Computing
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Pattern-based Representation (PBR) is a novel approach to improving the performance of Sparse Matrix-Vector Multiply (SMVM) numerical kernels. Motivated by our observation that many matrices can be divided into blocks that share a small number of distinct patterns, we generate custom multiplication kernels for frequently recurring block patterns. The resulting reduction in index overhead significantly reduces memory bandwidth requirements and improves performance. Unlike existing methods, PBR requires neither detection of dense blocks nor zero filling, making it particularly advantageous for matrices that lack dense nonzero concentrations. SMVM kernels for PBR can benefit from explicit prefetching and vectorization, and are amenable to parallelization. We present sequential and parallel performance results for PBR on two current multicore architectures, which show that PBR outperforms available alternatives for the matrices to which it is applicable.