Simulating spin models on GPU

Abstract

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 benefit from this setup for general computing purposes, the problems at hand need to be prepared in a way to profit 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.

5 pages, 4 figures, elsarticle

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• Harvesting graphics power for MD simulations
• JANUS: an FPGA-based System for High Performance Scientific Computing
• The Cluster Processor: New Results
• Performance potential for simulating spin models on GPU
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• An OpenCL implementation for the solution of TDSE on GPU and CPU architectures
• Superdiffusion-like behavior in zero-temperature coarsening of the $d=3$ Ising model
• A High-Performance Implementation of Atomistic Spin Dynamics Simulations on x86 CPUs
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• Parallelization and implementation of multi-spin Monte Carlo simulation of 2D square Ising model using MPI and C++
• Massive-Scale Simulations of 2D Ising and Blume-Capel Models on Rack-Scale Multi-GPU Systems
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