Gyrokinetic particle-in-cell optimization on emerging multi- and manycore platforms

Journal Article · Wed Mar 02 00:00:00 EST 2011 · Parallel Computing

DOI:https://doi.org/10.1016/j.parco.2011.02.001· OSTI ID:1407105

Madduri, Kamesh ^[1]; Im, Eun-Jin ^[2]; Ibrahim, Khaled Z. ^[1]; Williams, Samuel ^[1]; Ethier, Stéphane ^[3]; Oliker, Leonid ^[1]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Kookmin Univ., Seoul (Korea, Republic of)
3. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

The next decade of high-performance computing (HPC) systems will see a rapid evolution and divergence of multi- and manycore architectures as power and cooling constraints limit increases in microprocessor clock speeds. Understanding efficient optimization methodologies on diverse multicore designs in the context of demanding numerical methods is one of the greatest challenges faced today by the HPC community. In this paper, we examine the efficient multicore optimization of GTC, a petascale gyrokinetic toroidal fusion code for studying plasma microturbulence in tokamak devices. For GTC’s key computational components (charge deposition and particle push), we explore efficient parallelization strategies across a broad range of emerging multicore designs, including the recently-released Intel Nehalem-EX, the AMD Opteron Istanbul, and the highly multithreaded Sun UltraSparc T2+. We also present the first study on tuning gyrokinetic particle-in-cell (PIC) algorithms for graphics processors, using the NVIDIA C2050 (Fermi). Our work discusses several novel optimization approaches for gyrokinetic PIC, including mixed-precision computation, particle binning and decomposition strategies, grid replication, SIMDized atomic floating-point operations, and effective GPU texture memory utilization. Overall, we achieve significant performance improvements of 1.3–4.7× on these complex PIC kernels, despite the inherent challenges of data dependency and locality. Finally, our work also points to several architectural and programming features that could significantly enhance PIC performance and productivity on next-generation architectures.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: Intel Corporation (United States); Microsoft Corporation (United States); National Research Foundation of Korea (NRF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); USDOE Office of Science (SC), Fusion Energy Sciences (FES) (SC-24)
• Grant/Contract Number: AC02-05CH11231; AC02-09CH11466
• OSTI ID: 1407105
• Journal Information: Parallel Computing, Journal Name: Parallel Computing Journal Issue: 9 Vol. 37; ISSN 0167-8191
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Fermi
code optimization
graphic processing units
manycore
multicore
particle-in-cell