Akila Gothandaraman

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Multicore processors and a variety of accelerators have allowed scientific applications to scale to larger problem sizes. We present a performance, design methodology, platform, and architectural comparison of several application accelerators executing a Quantum Monte Carlo application. We compare the application's performance and programmability on a(More)
We are currently exploring the use of reconfigurable computing using Field Programmable Gate Arrays (FPGAs) to accelerate kernels of scientific applications. Here, we present a hardware architecture targeted towards the acceleration of two scientific kernels in a Quantum Monte Carlo (QMC) application applied to N-body systems. Quantum Monte Carlo methods(More)
Recent advances in FPGA technology make them an attractive platform for accelerating scientific computing applications. We present a novel hardware accelerator for Quantum Monte Carlo simulations in N-body systems. The design is deeply pipelined and exploits the inherent fine-grained parallelism available using an FPGA for all calculations. The design is(More)
Recent technological advances have led to a number of emerging platforms such as multi-cores, reconfigurable computing, and graphics processing units. We present a comparative study of multi-cores, field-programmable gate arrays, and graphics processing units for a Quantum Monte Carlo chemistry application. The speedups of these implementations are measured(More)
We propose a novel disc-based data decomposition algorithm for N-body simulations and compare its performance against a cyclic decomposition algorithm. We implement the data decomposition algorithms towards the calculation of three-body interactions in the Stillinger-Weber potential for a system of water molecules. The performance is studied in terms of(More)
In this paper, we explore the use of Graphics Processing Units (GPUs) to solve numerically the nonlinear Gross-Pitaevskii equation with an external potential. Our implementation uses NVIDIA's Compute Unified Device Architecture (CUDA) programming paradigm and demonstrates a speedup of 190x on an NVIDIA Tesla C2050 (Fermi) GPU compared to an optimized(More)
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