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A Unified Framework for Numerical and Combinatorial Computing

Published: 01 March 2008 Publication History

Abstract

A rich variety of tools help researchers with high-performance numerical computing, but few tools exist for large-scale combinatorial computing. The authors describe their efforts to build a common infrastructure for numerical and combinatorial computing by using parallel sparse matrices to implement parallel graph algorithms.

Cited By

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  • (2024)Dedicated Hardware Accelerators for Processing of Sparse Matrices and Vectors: A SurveyACM Transactions on Architecture and Code Optimization10.1145/364054221:2(1-26)Online publication date: 17-Jan-2024
  • (2024)Sparse-Sparse Matrix Multiplication Accelerator on FPGA featuring Distribute-Merge Product DataflowProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473865(785-791)Online publication date: 22-Jan-2024
  • (2023)A Systematic Survey of General Sparse Matrix-matrix MultiplicationACM Computing Surveys10.1145/357115755:12(1-36)Online publication date: 2-Mar-2023
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Published In

cover image Computing in Science and Engineering
Computing in Science and Engineering  Volume 10, Issue 2
March 2008
85 pages

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 March 2008

Author Tags

  1. HPC
  2. combinatorial
  3. combinatorics
  4. combinatorics in computing
  5. computing
  6. high-level languages
  7. high-performance computing
  8. sparse matrix

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Cited By

View all
  • (2024)Dedicated Hardware Accelerators for Processing of Sparse Matrices and Vectors: A SurveyACM Transactions on Architecture and Code Optimization10.1145/364054221:2(1-26)Online publication date: 17-Jan-2024
  • (2024)Sparse-Sparse Matrix Multiplication Accelerator on FPGA featuring Distribute-Merge Product DataflowProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473865(785-791)Online publication date: 22-Jan-2024
  • (2023)A Systematic Survey of General Sparse Matrix-matrix MultiplicationACM Computing Surveys10.1145/357115755:12(1-36)Online publication date: 2-Mar-2023
  • (2023)An Efficient Gustavson-Based Sparse Matrix–Matrix Multiplication Accelerator on Embedded FPGAsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.328171942:12(4671-4680)Online publication date: 1-Dec-2023
  • (2022)Parallel Algorithms for Masked Sparse Matrix-Matrix ProductsProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545048(1-11)Online publication date: 29-Aug-2022
  • (2022)PrGEMM: A Parallel Reduction SpGEMM AcceleratorProceedings of the Great Lakes Symposium on VLSI 202210.1145/3526241.3530387(397-401)Online publication date: 6-Jun-2022
  • (2022)A Pattern-Based SpGEMM Library for Multi-Core and Many-Core ArchitecturesIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309032833:1(159-175)Online publication date: 1-Jan-2022
  • (2021)Automatic Sublining for Efficient Sparse Memory AccessesACM Transactions on Architecture and Code Optimization10.1145/345214118:3(1-23)Online publication date: 10-May-2021
  • (2021)A Compressed, Divide and Conquer Algorithm for Scalable Distributed Matrix-Matrix MultiplicationThe International Conference on High Performance Computing in Asia-Pacific Region10.1145/3432261.3432271(110-119)Online publication date: 20-Jan-2021
  • (2021)Efficient execution of graph algorithms on CPU with SIMD extensionsProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370326(262-276)Online publication date: 27-Feb-2021
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