News of the group on Computational and Statistical Physics
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Multicanonical simulations on GPU
New method tackles first-order transitions with GPU power
In recent work with Jonathan Gross, Johannes Zierenberg and Wolfhard Janke from the University of Leipzig we propose a new massively parallel version of the well-established multicanonical simulation method ideally suited for systems undergoing discontinuous phase transitions. We demonstrate scaling with super-linear speedup for up to several ten thousand threads.
»Generalized-ensemble Monte Carlo simulations such as the multicanonical method and similar techniques are among the most efficient approaches for simulations of systems undergoing discontinuous phase transitions or with rugged free-energy landscapes. As Markov chain methods, they are inherently serial computationally. It was demonstrated recently, however, that a combination of independent simulations that communicate weight updates at variable intervals allows for the efficient utilization of parallel computational resources for multicanonical simulations. Implementing this approach for the many-thread architecture provided by current generations of graphics processing units (GPUs), we show how it can be efficiently employed with of the order of 104 parallel walkers and beyond, thus constituting a versatile tool for Monte Carlo simulations in the era of massively parallel computing. We provide the fully documented source code for the approach applied to the paradigmatic example of the two-dimensional Ising model as starting point and reference for practitioners in the field.«