Efficient implementation of the overlap operator on multi-GPUs - INSPIRE

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Efficient implementation of the overlap operator on multi-GPUs

Andrei Alexandru
(
- George Washington U.
)
,
Michael Lujan
(
- George Washington U.
)
,
Craig Pelissier
(
- George Washington U.
)
,
Ben Gamari
(
- George Washington U.
)
,
Frank X. Lee
(
- George Washington U.
)

Jun, 2011

8 pages

Part of Proceedings, 2011 Symposium on Application Accelerators in High-Performance Computing (SAAHPC'11) : Knoxville, Tennessee, July 19-20, 2011, 123-130

Published in:

IEEE Nucl.Sci.Symp.Conf.Rec.

Contribution to:

2011 Symposium on Application Accelerators in High-Performance Computing (SAAHPC'11), 123-130

Published: 2011

e-Print:

1106.4964 [hep-lat]

DOI:

10.1109/SAAHPC.2011.13

View in:

ADS Abstract Service

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Abstract: (arXiv)

Lattice QCD calculations were one of the first applications to show the potential of GPUs in the area of high performance computing. Our interest is to find ways to effectively use GPUs for lattice calculations using the overlap operator. The large memory footprint of these codes requires the use of multiple GPUs in parallel. In this paper we show the methods we used to implement this operator efficiently. We run our codes both on a GPU cluster and a CPU cluster with similar interconnects. We find that to match performance the CPU cluster requires 20-30 times more CPU cores than GPUs.

Lattice QCD
GPU
overlap
computer graphic equipment
coprocessors
parallel processing
quantum chromodynamics
CPU cluster
GPU cluster
QCD calculation

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