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Manuscript Title: A Hardware-Accelerated Quantum Monte Carlo Framework (HAQMC) for N-body Systems
Authors: Akila Gothandaraman, Gregory D. Peterson, G. Lee Warren, Robert J. Hinde, Robert J. Harrison
Program title: Hardware Accelerated Quantum Monte Carlo (HAQMC)
Catalogue identifier: AEEP_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 180(2009)2563
Programming language: C/C++ for the QMC application, VHDL and Xilinx 8.1 ISE/EDK tools for FPGA design and development.
Computer: Cray XD1 consisting of a dual-core, dualprocessor AMD Opteron 2.2 GHz with a Xilinx Virtex-4 (V4LX160) or Xilinx Virtex-II Pro (XC2VP50) FPGA per node. We use the compute node with the Xilinx Virtex-4 FPGA.
Operating system: Red Hat Enterprise Linux OS.
Has the code been vectorised or parallelized?: Yes
Keywords: Reconfigurable Computing, Field-Programmable Gate Arrays, FPGA, Cray XD1, Monte Carlo, Quantum Monte Carlo.
PACS: 2.70.Ss, 07.05.Bx, 07.05.Tp.
Classification: 6.1.

Nature of problem:
Quantum Monte Carlo is a practical method to solve the Schrödinger equation for large many-body systems and obtain the ground-state properties of such systems. This method involves the sampling of a number of configurations of atoms and averaging the properties of the configurations over a number of iterations. We are interested in applying the QMC method to obtain the energy and other properties of highly quantum clusters, such as inert gas clusters.

Solution method:
The proposed framework provides a combined hardware-software approach, in which the QMC simulation is performed on the host processor, with the computationally intensive functions such as energy and trial wave function computations mapped onto the field-programmable gate array (FPGA) logic device attached as a co-processor to the host processor. We perform the QMC simulation for a number of iterations as in the case of our original software QMC approach, to reduce the statistical uncertainty of the results. However, our proposed HAQMC framework accelerates each iteration of the simulation, by significantly reducing the time taken to calculate the ground-state properties of the configurations of atoms, thereby accelerating the overall QMC simulation. We provide a generic interpolation framework that can be extended to study a variety of pure and doped atomic clusters, irrespective of the chemical identities of the atoms. For the FPGA implementation of the properties, we use a two-region approach for accurately computing the properties over the entire domain, employ deep pipelines and fixed-point for all our calculations guaranteeing the accuracy required for our simulation.