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Acceleration of Quantum Monte Carlo Applications on Emerging Computing Platforms

Akila Gothandaraman  (University of Tennessee, Knoxville)
ACM Student Competition Session
Tuesday,  05:15PM - 07:00PM
Room Rotunda Lobby
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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