Parallel Algorithms for Geometric Connected Component Labeling on a Hypercube Multiprocessor
IEEE Transactions on Computers
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Introduction to Algorithms
GPU-based single-cluster algorithm for the simulation of the Ising model
Journal of Computational Physics
Performance potential for simulating spin models on GPU
Journal of Computational Physics
From GPGPU to Many-Core: Nvidia Fermi and Intel Many Integrated Core Architecture
Computing in Science and Engineering
Intel Xeon Phi Coprocessor High Performance Programming
Intel Xeon Phi Coprocessor High Performance Programming
| Hi-index | 0.00 |
Simulations of the critical Ising model by means of local update algorithms suffer from critical slowing down. One way to partially compensate for the influence of this phenomenon on the runtime of simulations is using increasingly faster and parallel computer hardware. Another approach is using algorithms that do not suffer from critical slowing down, such as cluster algorithms. This paper reports on the Swendsen-Wang multi-cluster algorithm on Intel Xeon Phi coprocessor 5110P, Nvidia Tesla M2090 GPU, and x86 multi-core CPU. We present shared memory versions of the said algorithm for the simulation of the two- and three-dimensional Ising model. We use a combination of local cluster search and global label reduction by means of atomic hardware primitives. Further, we describe an MPI version of the algorithm on Xeon Phi and CPU, respectively. Significant performance improvements over known implementations of the Swendsen-Wang algorithm are demonstrated.