NAG Numerical Routines for GPUs (Beta)

Routine Documentation

Accompanying the NAG Numerical Routines for GPUs is documentation, including an Introduction that gives background to the available Random Number Generators and recommendations on choice and use of routines. Further individual routine documentation expands on the functions showing purpose, specification, arguments, error indicators and warnings and examples.

Click to view the NAG Numerical Routines for GPUs Documentation

NAG Numerical Routines for GPUs Chapter Introduction
Constructs Brownian Bridge Sample Paths
Frees System Resources For Brownian Bridge
Initializes Brownian Bridge Generator
Computes Scaled Increments of a Brownian Bridge
Initializes the Brownian Bridge Increment Generator
Multiple Recursive Generator: Exponential Distribution
Initializes the Multiple Recursive Generator
Multiple Recursive Generator: Normal Distribution
Multiple Recursive Generator: Uniform Distribution
Frees System Resources For Sobol Sequence Generator
Sobol Sequence Generator: Exponential Distribution
Initializes the Sobol Generator
Sobol Sequence Generator: Normal Distribution
Sobol Sequence Generator: Uniform Distribution

 How can I gain access to NAG Numerical Routines for GPUs prototype?

The NAG Numerical Routines for GPUs prototype are available as a beta release. Currently, the prototype includes components for Monte Carlo simulation, comprising a pseudorandom number generator (L'Ecuyer, MRG32k3a), a quasi-random sequence generator (Sobol with extension to 50,000 dimensions) and a Brownian bridge constructor. The random number generators provide three output distributions (uniform, exponential and Normal) implemented in both single and double precisions.

Eligibility?

It is available free to academics subject to them signing a collaborative agreement. NAG is also keen to engage with commercial clients for assessment and further development of numerical routines for GPUs

To obtain a copy of the software please complete the online registration of interest form here or contact us directly for more information.

Ongoing work

NAG is continuing to develop further routines for GPUs including univariate and multi-variate distributions, copulas amongst others.

Acknowledgements

We would like to thank the Technology Strategy Board (TSB) and the Smith Institute for their support in sponsoring this project and EPSRC for supporting Professor Giles’ academic research