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We present an adaptive multi-GPU Exchange Monte Carlo method designed for thesimulation of the 3D Random Field Model. The algorithm design is based on atwo-level parallelization scheme that allows the method to scale itsperformance in the presence of faster and GPUs as well as multiple GPUs. Theset of temperatures is adapted according to the exchange rate observed fromshort trial runs, leading to an increased exchange rate at zones where theexchange process is sporadic. Performance results show that parallel temperingis an ideal strategy for being implemented on the GPU, and runs between one totwo orders of magnitude with respect to a single-core CPU version, withmulti-GPU scaling being approximately $99\%$ efficient. The results obtainedextend the possibilities of simulation to sizes of $L = 32, 64$ for aworkstation with two GPUs.

Adaptive Multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model