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SCHEDULE: NOV 12-18, 2011

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Extracting Ultra-Scale Lattice Boltzmann Performance via Hierarchical and Distributed Auto-Tuning

SESSION: Application Performance


TIME: 11:00AM - 11:30AM

AUTHOR(S):Samuel Williams, Leonid Oliker, Jonathan Carter, John Shalf


We are witnessing a rapid evolution of HPC node architectures and on-chip parallelism as power and cooling limit increases in microprocessor clock speeds. We demonstrate a hierarchical approach towards effectively extracting performance for a variety of emerging multicore-based supercomputing platforms. Our examined application is a structured grid-based Lattice Boltzmann computation that simulates homogeneous isotropic turbulence in magnetohydrodynamics. First, we examine auto-tuning techniques including loop transformations, virtual vectorization, use of ISA-specific intrinsics, including programming model exploration (flat MPI, MPI-OpenMP, and MPI-Pthreads), as well as data and thread decomposition strategies designed to mitigate communication bottlenecks. We evaluate the impact of our hierarchical tuning techniques using a variety of problem sizes via large-scale simulations on state-of-the-art Cray XT4, Cray XE6, and IBM BlueGene/P platforms. Results show that our approach improves performance and energy by up to 3.4x using 49,152 cores, while providing a portable optimization methodology for a variety of numerical methods.

Chair/Author Details:

Samuel Williams - Lawrence Berkeley National Laboratory

Leonid Oliker - Lawrence Berkeley National Laboratory

Jonathan Carter - Lawrence Berkeley National Laboratory

John Shalf - Lawrence Berkeley National Laboratory

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The full paper can be found in the ACM Digital Library

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