Extracting ultra-scale Lattice Boltzmann performance via hierarchical and distributed auto-tuning

We are witnessing a rapid evolution of HPC node architectures and on-chip parallelism as power and cooling constraints limit increases in microprocessor clock speeds. In this work, we demonstrate a hierarchical approach towards effectively extracting performance for a variety of emerging multicore-b...

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Bibliographic Details
Published in:2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC) pp. 1 - 12
Main Authors: Williams, Samuel, Oliker, Leonid, Carter, Jonathan, Shalf, John
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 12.11.2011
IEEE
Series:ACM Conferences
Subjects:
Auto-tuning
BlueGene
Distribution functions
Hybrid Programming Models
Lattice Boltzmann
Lattices
Mathematics of computing > Mathematical analysis > Numerical analysis
Mathematics of computing > Mathematical analysis > Numerical analysis > Numerical differentiation
Mathematics of computing > Mathematical software
Multicore processing
OpenMP
Optimization
SIMD
Theory of computation > Design and analysis of algorithms
Three dimensional displays
Tuning
Vectors
hybrid programming models
BlueGene
Lattice Boltzmann
SIMD
OpenMP
auto-tuning
ISBN:145030771X, 9781450307710
ISSN:2167-4329
Online Access:Get full text
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Summary:We are witnessing a rapid evolution of HPC node architectures and on-chip parallelism as power and cooling constraints limit increases in microprocessor clock speeds. In this work, 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 sophisticated sequential auto-tuning techniques including loop transformations, virtual vectorization, and use of ISA-specific intrinsics. Next, we present a variety of parallel optimization approaches including programming model exploration (flat MPI, MPI/OpenMP, and MPI/Pthreads), as well as data and thread decomposition strategies designed to mitigate communication bottlenecks. Finally, 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 unique tuning approach improves performance and energy requirements by up to 3.4x using 49,152 cores, while providing a portable optimization methodology for a variety of numerical methods on forthcoming HPC systems.
ISBN:145030771X
9781450307710
ISSN:2167-4329
DOI:10.1145/2063384.2063458