Parallel random number generators for sequences uniformly distributed over any range of integers

2006 ◽  
Vol 53 (7) ◽  
pp. 1496-1505 ◽  
Author(s):  
R. Kuehnel ◽  
J. Theiler ◽  
Yuke Wang
Keyword(s):  
Random Number ◽  
Random Number Generators ◽  
Parallel Random Number Generators

GENERATING PARALLEL RANDOM NUMBER GENERATORS BY CELLULAR PROGRAMMING

1996 ◽  
Vol 07 (02) ◽  
pp. 181-190 ◽  
Author(s):  
MOSHE SIPPER ◽  
MARCO TOMASSINI
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Programming Algorithm ◽  
Random Numbers ◽  
Random Number Generators ◽  
Parallel Random Number Generators

Random numbers are needed in a variety of applications, yet finding good random number generators is a difficult task. In this paper non-uniform cellular automata (CA) are studied, presenting the cellular programming algorithm for co-evolving such CAs to perform computations. The algorithm is applied to the evolution of random number generators; our results suggest that evolved generators are at least as good as previously described CAs, with notable advantages arising from the existence of a "tunable" algorithm for obtaining random number generators.

An OpenCL library for parallel random number generators

2019 ◽  
Vol 75 (7) ◽  
pp. 3866-3881
Author(s):  
Tadej Ciglarič ◽  
Rok Češnovar ◽  
Erik Štrumbelj
Keyword(s):  
Random Number ◽  
Random Number Generators ◽  
Parallel Random Number Generators

scholarly journals Implementation of Hardware-Accelerated Scalable Parallel Random Number Generators

VLSI Design ◽  
2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
JunKyu Lee ◽  
Gregory D. Peterson ◽  
Robert J. Harrison ◽  
Robert J. Hinde
Keyword(s):  
Monte Carlo ◽  
Reconfigurable Computing ◽  
Random Number ◽  
Random Number Generation ◽  
Random Number Generators ◽  
Fine Grained ◽  
Number Generation ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Parallel Random Number Generators

The Scalable Parallel Random Number Generators (SPRNGs) library is widely used in computational science applications such as Monte Carlo simulations since SPRNG supports fast, parallel, and scalable random number generation with good statistical properties. In order to accelerate SPRNG, we develop a Hardware-Accelerated version of SPRNG (HASPRNG) on the Xilinx XC2VP50 Field Programmable Gate Arrays (FPGAs) in the Cray XD1 that produces identical results. HASPRNG includes the reconfigurable logic for FPGAs along with a programming interface which performs integer random number generation. To demonstrate HASPRNG for Reconfigurable Computing (RC) applications, we also develop a Monte Carlo π-estimator for the Cray XD1. The RC Monte Carlo π-estimator shows a 19.1× speedup over the 2.2 GHz AMD Opteron processor in the Cray XD1. In this paper we describe the FPGA implementation for HASPRNG and a π-estimator example application exploiting the fine-grained parallelism and mathematical properties of the SPRNG algorithm.

HASPRNG: Hardware Accelerated Scalable Parallel Random Number Generators

2009 ◽  
Vol 180 (12) ◽  
pp. 2574-2581 ◽  
Author(s):  
JunKyu Lee ◽  
Yu Bi ◽  
Gregory D. Peterson ◽  
Robert J. Hinde ◽  
Robert J. Harrison
Keyword(s):  
Random Number ◽  
Random Number Generators ◽  
Parallel Random Number Generators
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