Vectorized algorithm for multidimensional Monte Carlo integration on modern GPU, CPU and MIC architectures

Abstract

The aim of this paper is to show that the multidimensional Monte Carlo integration can be efficiently implemented on computers with modern multicore CPUs and manycore accelerators including Intel MIC and GPU architectures using a new vectorized version of LCG pseudorandom number generator which requires limited amount of memory. We introduce two new implementations of the algorithm based on directive-based parallel programming standards OpenMP and OpenACC and consider their performance using Hockney-Jesshope theoretical model of vector computations. We also present and discuss the results of experiments performed on dual-processor Intel Xeon E5-2670 computers with Intel Xeon Phi 7120P and NVIDIA K40m.

Influence of loop transformations on performance and energy consumption of the multithreded WZ factorization

“…The key to software optimization in terms of the performance for algorithms that include nested loops is the right choice of the appropriate loop transformations. Loop transformations are a research topic for various automatic optimization techniques [8], [10], [14], [15] as well as for manual conversion of application code [5], [17], [18] so as to obtain the best possible performance on modern multi-core architectures.…”

Thin-Threads: An Approach for History-Based Monte Carlo on GPUs

“…Work by Szalkowski at al. [19] focused on the distributed parallel generation of random numbers for multidimensional integration, while Stpiczynski et al [18] emphasized vectorized approaches for CPUs, GPUs, and MIC architectures for this same problem. Monte Carlo algorithms are all similar in that they use random numbers to accomplish a task.…”