The multi-GPU open-source package QCDGPU for lattice Monte Carlo simulations of pure SU(N) gluody- namics in external magnetic field at finite temperature and O(N) model is developed. The code is implemented in OpenCL, tested on AMD and NVIDIA GPUs, AMD and Intel CPUs and may run on other OpenCL-compatible devices. The pack- age contains minimal external library dependencies and is OS platform-independent. It is optimized for heterogeneous computing due to the possibility of dividing the lattice into non-equivalent parts to hide the difference in performances of the devices used. QCDGPU has client-server part for distributed simulations. The package is designed to produce lattice gauge configurations as well as to analyze previously generated ones. QCDGPU may be executed in fault-tolerant mode. Monte Carlo procedure core is based on PRNGCL library for pseudo-random numbers generation on OpenCL-compatible devices, which contains several most popular pseudo-random number generators.
The first paper relating application of GPUs in HEP lattice simulations was published in 2007 @cite_1 . The authors of this work used OpenGL as programming language and for the first time denoted the need to store lattice data in the form of four-component vectors. Shortly after of the publication, NVIDIA unveiled a new architecture CUDA and OpenGL ceased to be used as a GPGPU-computation language in further works.
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Lattice Boltzmann Methods (LBM) are used for the computational simulation of Newtonian fluid dynamics. LBM-based simulations are readily parallelizable; they have been implemented on general-purpose processors, field-programmable gate arrays (FPGAs), and graphics processing units (GPUs). Of the three methods, the GPU implementations achieved the highest simulation performance per chip. With memory bandwidth of up to 141 GB s and a theoretical maximum floating point performance of over 600 GFLOPS, CUDA-ready GPUs from NVIDIA provide an attractive platform for a wide range of scientific simulations, including LBM. This paper improves upon prior single-precision GPU LBM results for the D3Q19 model by increasing GPU multiprocessor occupancy, resulting in an increase in maximum performance by 20 , and by introducing a space-efficient storage method which reduces GPU RAM requirements by 50 at a slight detriment to performance. Both GPU implementations are over 28 times faster than a single-precision quad-core CPU version utilizing OpenMP.