q-state Potts model metastability study using optimized GPU-based Monte Carlo algorithms

January 23rd, 2011

Abstract:

We implemented a GPU based parallel code to perform Monte Carlo simulations of the two dimensional q-state Potts model. The algorithm is based on a checkerboard update scheme and assigns independent random number generators to each thread (one thread per spin). The implementation allows to simulate systems up to ~10^9 spins with an average time per spin flip of 0.147ns on the fastest GPU card tested, representing a speedup up to 155x, compared with an optimized serial code running on a standard CPU. The possibility of performing high speed simulations at large enough system sizes allowed us to provide a positive numerical evidence about the existence of metastability on very large systems based on Binder’s criterion, namely, on the existence or not of specific heat singularities at spinodal temperatures different of the transition one.

(Ezequiel E. Ferrero, Juan Pablo De Francesco, Nicolás Wolovick and Sergio A. Cannas: “q-state Potts model metastability study using optimized GPU-based Monte Carlo algorithms”. [arXiv:1101.0876] [code and additional information])

TRNG-A library for parallel Monte Carlo on NVIDIA graphics cards

January 23rd, 2011

Tina’s Random Number Generator Library (TRNG) version 4.11 has been released. TRNG is a state of the art open-source C++ pseudo-random number generator library for sequential and parallel Monte Carlo simulations. Its design principles are based on a proposal for an extensible random number generator facility that will be part of the forthcoming revision of the ISO C++ standard. The TRNG library features an object oriented design, is easy to use and has been speed optimized. Its implementation does not depend on any communication library or hardware architecture. TRNG is suited for shared memory as well as for distributed memory computers and may be used in various parallel programming environments, e.g. Message Passing Interface Standard or OpenMP. As an outstanding new feature of the latest TRNG release 4.11 it also supports CUDA. All generators that are implemented by TRNG have been subjected to thorough statistical tests in sequential and parallel setups. Download and further information: http://trng.berlios.de/

Graphic processors to speed-up simulations for the design of high performance solar receptors

September 4th, 2007

This paper by Collange et al. at Université de Perpignan, France, decribes a prototype to be integrated into simulation codes that estimate temperature, velocity and pressure to design next generation solar receptors. Such codes delegate to GPUs the computation of heat transfer due to radiation. The authors use Monte-Carlo line-by-line ray-tracing through finite volumes. This means data-parallel arithmetic transformations on large data structures. The performance on two recent graphics cards (Nvidia 7800GTX and ATI RX1800XL) show speedups higher than 400 compared to CPU implementations leaving most of CPU computing resources available. As there were some questions pending about the accuracy of the operators implemented in GPUs, the authors start this report with a survey and some contributed tests on the various floating point units available on GPUs. (Graphic processors to speed-up simulations for the design of high performance solar receptors. S. Collange, M. Daumas, D. Defour. Proceedings of the IEEE 18th International Conference on Application-specific Systems, Architectures and Processors.)