The study of disordered spin systems through Monte Carlo simulations has proven to be a hard task due to the adverse energy landscape present at the low temperature regime, making it difficult for the simulation to escape from a local minimum. Replica based algorithms such as the Exchange Monte Carlo (also known as parallel tempering) […]

August 27, 2015 by hgpu

We present and compare the performances of two many-core architectures: the Nvidia Kepler and the Intel MIC both in a single system and in cluster configuration for the simulation of spin systems. As a benchmark we consider the time required to update a single spin of the 3D Heisenberg spin glass model by using the […]

February 14, 2014 by hgpu

The resolution of dynamics in out of equilibrium quantum spin systems relies at the heart of fundamental questions among Quantum Information Processing, Statistical Mechanics and Nano-Technologies. Efficient computational simulations of interacting many-spin systems are extremely valuable tools for tackling such questions. Here, we use the Trotter-Suzuki (TS) algorithm, a well-known strategy that provides the evolution […]

May 2, 2013 by hgpu

We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling algorithms recently proposed with CUDA for the assignment of the cluster in the Swendsen-Wang algorithm. Starting with the q-state Potts model, we extend our implementation to […]

February 6, 2012 by hgpu

We present a set of possible implementations for Graphics Processing Units (GPU) of the Overrelaxation technique applied to the 3D Heisenberg spin glass model. The results show that a carefully tuned code can achieve more than 100 GFlops/sec. of sustained performance and update a single spin in about 0.6 nanoseconds. A multi-hit technique that exploits […]

March 9, 2011 by hgpu

We describe different implementations of the 3D Heisenberg spin glass model for Graphics Processing Units (GPU). The results show that the fast shared memory gives better performance with respect to the slow global memory only if a multi-hit technique is used.

October 28, 2010 by hgpu