An LA-SiGMA Software Distribution GPU Accelerated Dynamical Cluster Approximation Hirsch-Fye Quantum Monte Carlo Version 0.9 (r000) - 17 April 2014 Copyright 2014 Louisiana State University This package contains source, documentation, and sample data for the original Fortran implementation of Dynamical Cluster Approximation (DCA) using Hirsch-Fye Quantum Monte Carlo (HFQMC) as the cluster solver and the GPU accelerated code. This code can be used to solve the Hubbard model for 1D, 2D and 3D cases at different fillings. And it can be easily adapted to simulate the Anderson lattice model. The code focuses on the square lattice where the so-called Betts clusters in addition to the usual cubic clusters are used. It should be straightforward to generalize to the triangular or honeycomb lattices. The DCA-HFQMC Fortran code is a massively parallel (MPI+OpenMP) Fortran code originally built by Mark Jarrell and his collaborators. To speed up the computation within the nowadays high-performance hardware structure, especially the Graphics Processing Unit (GPU) architecture, parts of the code have being rewritten in CUDA to enable large data re-use and accelerate the calculation. The GPU's sheer number of concurrent parallel computations and large bandwidth to many shared memories takes advantage of the inherent parallelism in the Green function update and measurement routines, and can substantially improve the efficiency of the HFQMC cluster solver. Step-by-step setup and operating instructions can be found in doc/instructions.txt. For the latest version and other resources visit http://lasigma.loni.org/package/hirsch-fye . LA-SiGMA, the Louisiana Alliance for Simulation-Guided Materials Applications, is a statewide interdisciplinary collaboration of material and computer scientists developing computational resources tuned for the latest generation of accelerator-equipped systems. The Alliance also develops graduate curricula and is engaged in multiple outreach activities. Visit us at http://lasigma.loni.org . The accelerator ports of this code were developed by Sameer Abu Asal, Conrad Moore, with help from Shuxiang Yang, Juana Moreno, and Mark Jarrell. This work was supported in part by the National Science Foundation under the NSF EPSCoR Cooperative Agreement No. EPS-1003897.