Simulating spin models on GPU

Over the last couple of years it has been realized that the vast computational power of graphics processing units (GPUs) could be harvested for purposes other than the video game industry. This power, which at least nominally exceeds that of current CPUs by large factors, results from the relative simplicity of the GPU architectures as compared to CPUs, combined with a large number of parallel processing units on a single chip. To benet from this setup for general computing purposes, the problems at hand need to be prepared in a way to prot from the inherent parallelism and hierarchical structure of memory accesses. In this contribution I discuss the performance potential for simulating spin models, such as the Ising model, on GPU as compared to conventional simulations on CPU.

[1]  H. Diep.  Frustrated Spin Systems , 2020 .

[2]  Kurt Binder,et al.  A Guide to Monte Carlo Simulations in Statistical Physics: Frontmatter , 2009 .

[3]  Stanimire Tomov,et al.  Benchmarking and implementation of probability-based simulations on programmable graphics cards , 2003, Comput. Graph..

[4]  J. Zinn-Justin.  Quantum Field Theory and Critical Phenomena , 2002 .

[5]  Benjamin Krill,et al.  QPACE: Quantum Chromodynamics Parallel Computing on the Cell Broadband Engine , 2008, Computing in Science & Engineering.

[6]  Firas Hamze,et al.  Importance of explicit vectorization for CPU and GPU software performance , 2010, J. Comput. Phys..

[7]  M. Fisher,et al.  Bounded and Inhomogeneous Ising Models. I. Specific-Heat Anomaly of a Finite Lattice , 1969 .

[8]  Wolfgang Paul,et al.  GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model , 2009, J. Comput. Phys..

[9]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[10]  Andrea Pelissetto,et al.  Critical phenomena and renormalization-group theory , 2002 .

[11]  T. Lippert,et al.  Quantum Chromodynamics Parallel Computing on the Cell Broadband Engine Computing in Science & Engineering , 2008 .

[12]  A. Arnold,et al.  Harvesting graphics power for MD simulations , 2007, 0709.3225.

[13]  Richard P. Brent,et al.  Uniform random number generators for supercomputers , 1992 .

[14]  K. Binder,et al.  A Guide to Monte Carlo Simulations in Statistical Physics , 2000 .

[15]  Lev N. Shchur,et al.  The Cluster Processor:. New Results , 1999 .

[16]  Kandel,et al.  General cluster Monte Carlo dynamics. , 1991, Physical review. B, Condensed matter.

[17]  Bernd A. Berg.  Markov Chain Monte Carlo Simulations and Their Statistical Analysis , 2004 .

[18]  A. Young,et al.  Spin glasses and random fields , 1997 .

[19]  Martin Weigel,et al.  Performance potential for simulating spin models on GPU , 2010, J. Comput. Phys..

[20]  David Thomas,et al.  The Art in Computer Programming , 2001 .

[21]  Donald E. Knuth,et al.  The art of computer programming. Vol.2: Seminumerical algorithms , 1981 .

[22]  Michael E. Fisher,et al.  Bounded and inhomogeneous Ising models. III. Regularly spaced point defects , 1976 .

[23]  H. Rieger,et al.  Recent Progress in Spin Glasses , 2003, cond-mat/0312432.

[24]  Denis Navarro,et al.  Janus: An FPGA-Based System for High-Performance Scientific Computing , 2007, Computing in Science & Engineering.