Population annealing is a promising recent approach for Monte Carlo simulations in statistical physics, in particular for the simulation of systems with complex free-energy landscapes. It is a hybrid method, combining importance sampling through Markov chains with elements of sequential Monte Carlo in the form of population control. While it appears to provide algorithmic capabilities for the simulation of such systems that are roughly comparable to those of more established approaches such as parallel tempering, it is intrinsically much more suitable for massively parallel computing. Here, we tap into this structural advantage and present a highly optimized implementation of the population annealing algorithm on GPUs that promises speed-ups of several orders of magnitude as compared to a serial implementation on CPUs. While the sample code is for simulations of the 2D ferromagnetic Ising model, it should be easily adapted for simulations of other spin models, including disordered systems. Our code includes implementations of some advanced algorithmic features that have only recently been suggested, namely the automatic adaptation of temperature steps and a multi-histogram analysis of the data at different temperatures. The program calculates the internal energy, specific heat, several magnetization moments, entropy and free energy of the 2D Ising model on square lattices of edge length L with periodic boundary conditions as a function of inverse temperature β. Solution method: The code uses population annealing, a hybrid method combining Markov chain updates with population control. The code is implemented for NVIDIA GPUs using the CUDA language and employs advanced techniques such as multi-spin coding, adaptive temperature steps and multi-histogram reweighting. Restrictions: The system size and size of the population of replicas are limited depending on the memory of the GPU device used. Unusual features: Additional comments: Code repository at https://github.com/LevBarash/PAising. Running time: For the default parameter values used in the sample programs, L = 64, θ = 100, β0 = 0, β f = 1, ∆β = 0.005, R = 20 000, a typical run time on an NVIDIA Tesla K80 GPU is 151 seconds for the single spin coded (SSC) and 17 seconds for the multi-spin coded (MSC) program (see Sec. 2 for a description of these parameters). PROGRAM SUMMARY

Magnetization processes and phase transitions in a geometrically frustrated triangular lattice Ising antiferromagnet in the presence of an external magnetic field and a random site dilution are studied by the use of an effective-field theory with correlations. We find that the interplay between the applied field and the frustration-relieving dilution results in peculiar phase diagrams in the temperaturefield-dilution parameter space.

The phase transitions occurring in the frustrated Ising square antiferromagnet with first- (J1<0) and second-nearest-neighbor (J2<0) interactions are studied within the framework of the effective-field theory with correlations based on the different cluster sizes and for a wide range of R=J2/|J1|. Despite the simplicity of the model, it has proved difficult to precisely determine the order of the phase transitions. In contrast to the previous effective-field study, we have found a first-order transition line in the region close to R=-0.5 not only between the superantiferromagnetic and paramagnetic (R<-0.5) but also between antiferromagnetic and paramagnetic (R>-0.5) phases.

We use the effective-field theory with correlations based on different cluster sizes to investigate phase diagrams of the frustrated Ising antiferromagnet on the honeycomb lattice with isotropic interactions of the strength J 1 < 0 between nearestneighbour pairs and J 2 < 0 between next-nearest neighbour pairs of spins. We present results for the ground-state energy as a function of the frustration parameter R = J 2 /|J 1 |. We find that the cluster-size has a considerable effect on the existence and location of a tricritical point in the phase diagram at which the phase transition changes from the second order to the first one.