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

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

2011-01-05

arxiv.org/abs/1101.0876v3

Physics

Condensed Matter

Statistical Mechanics

30 pages, 7 figures. Accepted in Computer Physics Communications. code available at: http://www.famaf.unc.edu.ar/grupos/GPGP

Scientific paper

10.1016/j.cpc.2012.02.026

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 numbers generators to each thread. 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 high-end 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.

Affiliated with

Also associated with

No associations

Rating

q-State Potts model metastability study using optimized GPU-based Monte Carlo algorithms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with q-State Potts model metastability study using optimized GPU-based Monte Carlo algorithms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and q-State Potts model metastability study using optimized GPU-based Monte Carlo algorithms will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-266121