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Automatic Performance Tuning of Sparse-Matrix-Vector-Multiplication (SpMV) and Iterative Sparse Solvers

Automatic Performance Tuning of Sparse-Matrix-Vector-Multiplication (SpMV) and Iterative Sparse Solvers

Automatic Performance Tuning of Sparse-Matrix-Vector-Multiplication (SpMV) and Iterative Sparse Solvers. James Demmel www.cs.berkeley.edu/~demmel/cs267_Spr09. Outline. Motivation for Automatic Performance Tuning Results for sparse matrix kernels Sparse Matrix Vector Multiplication (SpMV)

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2.9k views • 261 slides
Auto-tuning Stencil Codes for Cache-Based Multicore Platforms

Auto-tuning Stencil Codes for Cache-Based Multicore Platforms

Auto-tuning Stencil Codes for Cache-Based Multicore Platforms. Kaushik Datta Dissertation Talk December 4, 2009. Motivation. Multicore revolution has produced wide variety of architectures Compilers alone fail to fully exploit multicore resources Hand-tuning has become infeasible

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528 views • 52 slides
Improving High Performance Sparse Libraries using Compiler-Assisted Specialization

Improving High Performance Sparse Libraries using Compiler-Assisted Specialization

Improving High Performance Sparse Libraries using Compiler-Assisted Specialization. By Shreyas Ramalingam. A PETSc Case Study. Outline. Outline. Motivation. Performance depends on execution context. Specializing code to execution context can improve performance significantly. Input Data.

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481 views • 41 slides
Online Adaptive Code Generation and Tuning

Online Adaptive Code Generation and Tuning

Online Adaptive Code Generation and Tuning. Jeffrey K. Hollingsworth and Ananta Tiwari. Why Automated Performance Tuning?. Software parameters impacts its performance. Optimal parameter values are variable and un-predictable. Parameters come from: User code Libraries Compiler choices

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324 views • 20 slides
Input-sensitivity Develop a greedy algorithm to select representative code variants

Input-sensitivity Develop a greedy algorithm to select representative code variants

Input sensitivity Study. Input-sensitivity Develop a greedy algorithm to select representative code variants. Overall results. Overall results. Overall results. Code variant sensitivity. What can we do?. Study the correlation among inputs dimensions? D=10

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165 views • 16 slides

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