Lattice Boltzmann Simulation Optimization on Leading Multicore Platforms

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We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to a lattice Boltzmann application (LBMHD) that historically has made poor use of scalar microprocessors due to its complex data structures and memory access patterns. We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Clovertown, AMD Opteron X2, Sun Niagara2, STI Cell, as well as the single core Intel Itanium2. Rather than hand-tuning LBMHD ... continued below

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Williams, Samuel; Carter, Jonathan; Oliker, Leonid; Shalf, John & Yelick, Katherine February 1, 2008.

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Description

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to a lattice Boltzmann application (LBMHD) that historically has made poor use of scalar microprocessors due to its complex data structures and memory access patterns. We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Clovertown, AMD Opteron X2, Sun Niagara2, STI Cell, as well as the single core Intel Itanium2. Rather than hand-tuning LBMHD for each system, we develop a code generator that allows us identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned LBMHD application achieves up to a 14x improvement compared with the original code. Additionally, we present detailed analysis of each optimization, which reveal surprising hardware bottlenecks and software challenges for future multicore systems and applications.

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  • International Parallel and Distributed Processing Symposium (IPDPS), 2008, Miami, FL, 04/14-28/2008

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  • Report No.: LBNL-2165E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 964372
  • Archival Resource Key: ark:/67531/metadc934240

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • February 1, 2008

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 18, 2016, 2:53 p.m.

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Williams, Samuel; Carter, Jonathan; Oliker, Leonid; Shalf, John & Yelick, Katherine. Lattice Boltzmann Simulation Optimization on Leading Multicore Platforms, article, February 1, 2008; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc934240/: accessed October 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.