New numeric routines for graphics chips now available from Numerical Algorithms Group

New numeric routines for graphics chips now available from Numerical Algorithms Group

Retail analysts involved in business process optimisation, predictive modelling and other retail analytics interested in achieving top performance from GPUs in diverse applications using Monte Carlo simulations can now obtain an updated version of Numerical Algorithms Group (NAG) numeric routines for graphics processing units (GPUs).

General purpose GPUs 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, vertical marketing general manager, commented: “The ecosystem around GPU computing is growing rapidly and NAG’s additions to their routines for GPU computing could not be more timely.

Adding accurate algorithms to GPU computing

“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 [Compute Unified Device Architecture] 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 organisation. Commercial organisations 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.