tronics engineers designing embedded systems and other electronics design projects who are interested in achieving top performance from GPUs in diverse applications using Monte Carlo simulations can now obtain an updated version of NAG numeric routines for GPUs from the Numerical Algorithms Group (NAG) .
General Purpose GPUs (graphical processing units) were originally used for 3D gaming acceleration on personal computers but have recently been at the forefront of numerical and scientific computation. Monte Carlo simulations are used in a wide array of technical computing applications in diverse areas such as finance, engineering simulations, drug discovery, scientific research, oil and gas exploration, and more.
Speaking for NVIDIA, a leader in GPU computing, Andrew Cresci, GM Vertical Marketing comments, “The ecosystem around GPU computing is growing rapidly and NAG’s additions to their routines for GPU computing could not be more timely. NAG’s numerical libraries are renowned for delivering top performance while maintaining the highest standards of accuracy. There are now some 60,000 active CUDA developers, and providing access to trusted algorithms from NAG is a major milestone that enhances the maturity of NVIDIA’s GPU computing architecture.”
NAG’s numerical routines for GPU computing are available to academic researchers involved in collaborative research with the NAG organization. Commercial organizations can also get access to NAG’s GPU code and programming services by contacting the NAG offices in their locale.
The latest release of NAG’s code for GPUs contains routines for Monte Carlo simulations—Quasi and Pseudo Random Number Generators, Brownian bridge, and associated statistical distributions.