Optimization of a Lattice Boltzmann Computation on State-of-the-Art 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 Xeon E5345 (Clovertown), AMD Opteron 2214 (Santa Rosa), AMD Opteron 2356 (Barcelona), Sun T5140 T2+ (Victoria Falls), as well as ... continued below

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Williams, Samuel; Carter, Jonathan; Oliker, Leonid; Shalf, John & Yelick, Katherine April 10, 2009.

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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 Xeon E5345 (Clovertown), AMD Opteron 2214 (Santa Rosa), AMD Opteron 2356 (Barcelona), Sun T5140 T2+ (Victoria Falls), as well as a QS20 IBM Cell Blade. Rather than hand-tuning LBMHD for each system, we develop a code generator that allows us to 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 15x improvement compared with the original code at a given concurrency. 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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  • Journal Name: Journal of Parallel and Distributed Computing

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  • Report No.: LBNL-2146E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 963653
  • Archival Resource Key: ark:/67531/metadc931710

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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.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • April 10, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 18, 2016, 3:04 p.m.

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Williams, Samuel; Carter, Jonathan; Oliker, Leonid; Shalf, John & Yelick, Katherine. Optimization of a Lattice Boltzmann Computation on State-of-the-Art Multicore Platforms, article, April 10, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc931710/: accessed May 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.