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Tuning Sparse Matrix Vector Multiplication For Multicore SMPs.pdf

williams2007-multicore-spmv-slides.pdf
Preview of Tuning Sparse Matrix Vector Multiplication for Multicore SMPs
🔗 Source: bebop.cs.berkeley.edu
📊 Size: 6.4 MB
👤 Author: samw
⬇️ Downloads: 163

Summary

Researchers from the University of California Berkeley, Lawrence Berkeley National Laboratory, and Georgia Institute of Technology examined Sparse Matrix Vector Multiplication (SpMV) kernel on multicore architectures, presenting two autotuned threaded implementations and benchmarking performance across four diverse architectures, comparing with leading MPI implementation. They found SpMV challenging due to irregular memory access, difficulty in exploiting instruction-level parallelism (ILP) and data-level parallelism (DLP), and low computational intensity. The study used a test suite of 14 sparse matrices, subdivided into four categories, to evaluate the performance of SpMV on multicore SMPs.

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Description

Researchers from the University of California Berkeley, Lawrence Berkeley National Laboratory, and Georgia Institute of Technology examined Sparse Matrix...

Technical Information

  • File Format: PDF
  • File Size: 6.4 MB
  • Pages: 41
  • Language: EN
  • Author: samw
  • Total Downloads: 163
  • Last Updated: 2 hours ago

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This PDF document about Tuning Sparse Matrix Vector Multiplication for Multicore SMPs provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Tuning Sparse Matrix Vector Multiplication for Multicore SMPs.

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Tuning guideTuning tutorialSparse guideSparse tutorialMatrix guideMatrix tutorialVector guideVector tutorialMultiplication guideMultiplication tutorialMulticore guideMulticore tutorialSMPs guideSMPs tutorial

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