Add to ORKGLimited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/random-numbers/gpu_park-miller.tar.g
We consider the requirements for uniform pseudo-random number generators on modern vector and parall...
There are many problems arises in randomized algorithms whose solutions are fundamentally based on a...
The future of high-performance computing is aligning itself towards the efficient use of highly para...
Anyone who considers arithmetical methods of producing random digits is, of course, in a state of si...
This work considers the deployment of pseudo-random number generators (PRNGs) on graphics processing...
The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number gener...
International audienceStochastic simulations are often sensitive to the source of randomness that ch...
International audienceParallel stochastic simulations tend to exploit more and more computing power ...
Abstract — Pseudo Random number Generator(PRNG) is used in various cryptographic applications such ...
Random number generators are essential in many computing applications, such as Artificial Intelligen...
This paper proposes a type of pseudorandom number generator,Mersenne Twister for Graphic Processor (...
Fast and reliable pseudo-random number generators are required for simulation and other applications...
Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in scie...
We consider the requirements for uniform pseudo-random number generators on modern vector and parall...
Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in scie...
We consider the requirements for uniform pseudo-random number generators on modern vector and parall...
There are many problems arises in randomized algorithms whose solutions are fundamentally based on a...
The future of high-performance computing is aligning itself towards the efficient use of highly para...
Anyone who considers arithmetical methods of producing random digits is, of course, in a state of si...
This work considers the deployment of pseudo-random number generators (PRNGs) on graphics processing...
The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number gener...
International audienceStochastic simulations are often sensitive to the source of randomness that ch...
International audienceParallel stochastic simulations tend to exploit more and more computing power ...
Abstract — Pseudo Random number Generator(PRNG) is used in various cryptographic applications such ...
Random number generators are essential in many computing applications, such as Artificial Intelligen...
This paper proposes a type of pseudorandom number generator,Mersenne Twister for Graphic Processor (...
Fast and reliable pseudo-random number generators are required for simulation and other applications...
Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in scie...
We consider the requirements for uniform pseudo-random number generators on modern vector and parall...
Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in scie...
We consider the requirements for uniform pseudo-random number generators on modern vector and parall...
There are many problems arises in randomized algorithms whose solutions are fundamentally based on a...
The future of high-performance computing is aligning itself towards the efficient use of highly para...