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Student Contribution |
SC Conference - Activity Details
Acceleration of Quantum Monte Carlo Applications on Emerging Computing Platforms
Author:
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Akila Gothandaraman
(University of Tennessee, Knoxville)
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ACM Student Competition Session
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Wednesday, 11:42AM - 12:00PM
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Room 17A/17B
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Abstract:
Recent technological advances have led to a number of emerging platforms, such
as multi-core processors, reconfigurable computing (RC), and graphics processing units (GPUs), which can boost the performance of scientific
applications. This work explores RC and GPU based platforms to exploit the best
features of each of these platforms for a Quantum Monte Carlo (QMC) application. We have demonstrated a speedup of 25x for the FPGA accelerated
kernels over the software-only QMC application on the Cray XD1 HPRC platform.
Here, we provide an outline of the computationally intensive kernels of the QMC application and a preliminary analytical performance model, which we are
extending to enable us to optimize the use of the emerging computational
resources to accelerate the kernels. We will also report on porting the kernels to
the nVidia Tesla GPUs, allowing us to exploit the tremendous data parallelism of
these platforms for our "inherently parallel" Monte Carlo simulations.
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