The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Linear and inversive pseudorandom numbers for parallel and distributed simulation
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
Uniform Random Number Generators
Journal of the ACM (JACM)
A user-programmable vertex engine
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Handbook of Applied Cryptography
Handbook of Applied Cryptography
An Object-Oriented Random-Number Package with Many Long Streams and Substreams
Operations Research
Programming Vertex and Pixel Shaders
Programming Vertex and Pixel Shaders
Modified noise for evaluation on graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
TestU01: A C library for empirical testing of random number generators
ACM Transactions on Mathematical Software (TOMS)
Parallel white noise generation on a GPU via cryptographic hash
Proceedings of the 2008 symposium on Interactive 3D graphics and games
State-of-the-art in heterogeneous computing
Scientific Programming
Particle filtering: the need for speed
EURASIP Journal on Advances in Signal Processing
Proceedings of the 14th International Conference on Extending Database Technology
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
High-performance pseudo-random number generation on graphics processing units
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
An effective and efficient parallel approach for random graph generation over GPUs
Journal of Parallel and Distributed Computing
| Hi-index | 0.00 |
Statistical algorithms such as Monte Carlo integration are good candidates to run on graphics processing units. The heart of these algorithms is random number generation, which generally has been done on the CPU. In this paper we present GPU implementations of three random number generators. We show how to overcome limitations of GPU hardware that affect the feasibility and efficiency of employing a GPU-based RNG. We also present a data flow model for managing and updating substream state for each of the parallel substreams of random numbers. We show that GPU random number generators will greatly benefit from having more outputs from each thread. We discuss other hardware modifications that will be beneficial to the implementation of GPU-RNG, and we present performance measurements of our implementations.