This work presents an updated and extended guide on methods of a proper acceleration of the Monte Carlo integration of stochastic differential equations with the commonly available NVIDIA Graphics Processing Units using the CUDA programming environment. We outline the general aspects of the scientific computing on graphics cards and demonstrate them with two models of […]

September 19, 2014 by hgpu

Problems in many areas give rise to computationally expensive integrals that beg the need of efficient techniques to solve them, e.g., in computational finance for the modeling of cash flows; for the computation of Feynman loop integrals in high energy physics; and in stochastic geometry with applications to computer graphics. We demonstrate feasible numerical approaches […]

August 3, 2014 by hgpu

The matrix element method utilizes ab initio calculations of probability densities as powerful discriminants for processes of interest in experimental particle physics. The method has already been used successfully at previous and current collider experiments. However, the computational complexity of this method for final states with many particles and degrees of freedom sets it at […]

August 2, 2014 by hgpu

Continuing our previous studies on QED and QCD processes, we use the graphics processing unit (GPU) for fast calculations of helicity amplitudes for general Standard Model (SM) processes. Additional HEGET codes to handle all SM interactions are introduced, as well assthe program MG2CUDA that converts arbitrary MadGraph generated HELAS amplitudess(FORTRAN) into HEGET codes in CUDA. […]

May 6, 2013 by hgpu

We present a new real-time importance sampling algorithm for environment maps. Our method is based on representing environment maps using kd-tree structures, and generating samples with a single data lookup. An efficient algorithm has been developed for realtime image-based lighting applications. In this paper, we compared our algorithm with Inversion method [Fishman 1996]. We show […]

September 15, 2012 by hgpu

We use a graphics processing unit (GPU) for fast computations of Monte Carlo integrations. Two widely used Monte Carlo integration programs, VEGAS and BASES, are parallelized on GPU. By using $W^+$ plus multi-gluon production processes at LHC, we test integrated cross sections and execution time for programs in FORTRAN and C on CPU and those […]

October 27, 2010 by hgpu