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Access Type
WSU Access
Date of Award
January 2012
Degree Type
Thesis
Degree Name
M.S.
Department
Computer Science
First Advisor
Loren Schwiebert
Abstract
Abstract
EFFICIENT RANDOM NUMBER GENERATION
FOR
FERMI CLASS GPUs
by
NIRODHA ABEYWARDANA
JAN 2012
Advisor: Dr. Loren Schwiebert
Major: Computer Science
Degree: Master of Science
High quality pseudorandom number generators are very important in computational science
applications such as Monte Carlo simulations in order to achieve quality results. As large-scale
Monte Carlo computation consumes large amounts of computational power, there has been much
research on modern Graphics Processing Units to improve efficiency. Parallel portions of computationally
intensive algorithms can be programmed on GPUs using Compute Unified Device
Architecture (CUDA) on NVIDIA GPUs.
Applicability of existing random number generators on Monte Carlo simulations that runs on
the GPU is limited as the transfer of generated random numbers from CPU to GPU is costly. We
propose E-MTGP, which is Mersenne Twister based random number generator that runs faster on
the GPU.We evaluate the performance of E-MTGP and show how we can use this for both type of
applications that run on GPU and CPU.
Recommended Citation
Abeywardana, Nirodha, "Efficient random number generation for fermi class gpus" (2012). Wayne State University Theses. 153.
https://digitalcommons.wayne.edu/oa_theses/153