POET: Parameterized Optimization for Empirical Tuning

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The excessive complexity of both machine architectures and applications have made it difficult for compilers to statically model and predict application behavior. This observation motivates the recent interest in performance tuning using empirical techniques. We present a new embedded scripting language, POET (Parameterized Optimization for Empirical Tuning), for parameterizing complex code transformations so that they can be empirically tuned. The POET language aims to significantly improve the generality, flexibility, and efficiency of existing empirical tuning systems. We have used the language to parameterize and to empirically tune three loop optimizations-interchange, blocking, and unrolling-for two linear algebra kernels. We show experimentally ... continued below

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10 p. (0.2 MB)

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Yi, Q; Seymour, K; You, H; Vuduc, R & Quinlan, D January 29, 2007.

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The excessive complexity of both machine architectures and applications have made it difficult for compilers to statically model and predict application behavior. This observation motivates the recent interest in performance tuning using empirical techniques. We present a new embedded scripting language, POET (Parameterized Optimization for Empirical Tuning), for parameterizing complex code transformations so that they can be empirically tuned. The POET language aims to significantly improve the generality, flexibility, and efficiency of existing empirical tuning systems. We have used the language to parameterize and to empirically tune three loop optimizations-interchange, blocking, and unrolling-for two linear algebra kernels. We show experimentally that the time required to tune these optimizations using POET, which does not require any program analysis, is significantly shorter than that when using a full compiler-based source-code optimizer which performs sophisticated program analysis and optimizations.

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10 p. (0.2 MB)

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PDF-file: 10 pages; size: 0.2 Mbytes

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  • Presented at: IPDPS Workshop on Performance Optimization of High-Level Languages and Libraries, Long Beach, CA, United States, Mar 30 - Mar 30, 2007

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  • Report No.: UCRL-PROC-227558
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 908082
  • Archival Resource Key: ark:/67531/metadc880784

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  • January 29, 2007

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • April 13, 2017, 3:07 p.m.

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Yi, Q; Seymour, K; You, H; Vuduc, R & Quinlan, D. POET: Parameterized Optimization for Empirical Tuning, article, January 29, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc880784/: accessed October 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.