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Issue No. 10 - Oct. (2012 vol. 23)
ISSN: 1045-9219
pp: 1915-1922
Kamesh Madduri , Lawrence Berkeley National Laboratory, Berkeley
Jimmy Su , University of California at Berkeley, Berkeley
Samuel Williams , Lawrence Berkeley National Laboratory, Berkeley
Leonid Oliker , Lawrence Berkeley National Laboratory, Berkeley
Stéphane Ethier , Princeton Plasma Physics Laboratory, Princeton
Katherine Yelick , University of California at Berkeley and Lawrence Berkeley National Laboratory, Berkeley
We are now in the multicore revolution which is witnessing a rapid evolution of architectural designs due to power constraints and correspondingly limited microprocessor clock speeds. Understanding how to efficiently utilize these systems in the context of demanding numerical algorithms is an urgent challenge to meet the ever growing computational needs of high-end computing. In this work, we examine multicore parallel optimization of the particle-to-grid interpolation step in particle-mesh methods, an inherently complex optimization problem due to its low computation intensity, irregular data accesses, and potential fine-grained data hazards. Our evaluated kernels are derived from two important numerical computations: a biological simulation of the heart using the Immersed Boundary (IB) method, and a Gyrokinetic Particle-in-Cell (PIC)-based application for studying fusion plasma microturbulence. We develop several novel synchronization and grid decomposition schemes, as well as low-level optimization techniques to maximize performance on three modern multicore platforms: Intel's Xeon X5550 (Nehalem), AMD's Opteron 2356 (Barcelona), and Sun's UltraSparc {\rm T}2{+} (Niagara). Results show that our optimizations lead to significant performance improvements, achieving up to a 5.6{\times} speedup compared to the reference parallel implementation. Our work also provides valuable insight into the design of future autotuning frameworks for particle-to-grid interpolation on next-generation systems.
Heart, Synchronization, Optimization, Interpolation, Multicore processing, Kernel, Computational modeling, lock free, Particle mesh, particle-to-grid interpolation, multicore performance tuning, synchronization, atomic

S. Ethier, L. Oliker, S. Williams, J. Su, K. Madduri and K. Yelick, "Optimization of Parallel Particle-to-Grid Interpolation on Leading Multicore Platforms," in IEEE Transactions on Parallel & Distributed Systems, vol. 23, no. , pp. 1915-1922, 2012.
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