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DHPC-144 JAPARA – A Java Parallel Random Number Generator Library for High-Performance Computing
, 2004
"... Random number generators are one of the most common numerical library functions used in scientific applications. The standard random number generator provided within Java is fine for most purposes, however it does not adequately meet the needs of large-scale scientific applications, such as Monte Ca ..."
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Random number generators are one of the most common numerical library functions used in scientific applications. The standard random number generator provided within Java is fine for most purposes, however it does not adequately meet the needs of large-scale scientific applications, such as Monte Carlo simulations. Previous work has addressed some of these problems by extending the standard Random API in Java and providing an implementation that includes a choice of several different generator algorithms. One issue that was not addressed in this work was concurrency. Implementations of the standard Java random number generator use synchronized methods to support the use of the generator across multiple Java threads, however this is a sequential bottleneck for parallel applications. Here we present a proposal for further extending the standard API to support parallel generation of random number streams,
Parallel Random Number Generators in Java
, 2003
"... Scientific computing has long been pushing the boundaries of computational requirements in computer science. An important aspect of scientific computing is the generation of large quantities of random numbers, especially in parallel to take advantage of parallel architectures. Many science and engin ..."
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Scientific computing has long been pushing the boundaries of computational requirements in computer science. An important aspect of scientific computing is the generation of large quantities of random numbers, especially in parallel to take advantage of parallel architectures. Many science and engineering programs require random numbers for applications like Monte Carlo simulation. Such an environment suitable for parallel computing is Java, though rarely used for scientific applications due to its perceived slowness when compared to complied languages like C. Through research and recommendations, Java is slowly being shaped into a viable language for such computational intense applications. Java has the potential for such large scale applications, since it is a modern language with a large programmer base and many well received features such as built-in support for parallelism using threads. With improved performance from better compilers, Java is becoming more commonly used for scientific computing but Java still lacks a number of features like optimised scientific software libraries. This project looks at the effectiveness and efficiency of implementing a parallel random number
(www.interscience.wiley.com) DOI: 10.1002/sim.2639 Trying to be precise about vagueness
"... A previous investigation by Lambert et al., which used computer simulation to examine the influence of choice of prior distribution on inferences from Bayesian random effects meta-analysis, is critically examined from a number of viewpoints. The practical example used is shown to be problematic. The ..."
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A previous investigation by Lambert et al., which used computer simulation to examine the influence of choice of prior distribution on inferences from Bayesian random effects meta-analysis, is critically examined from a number of viewpoints. The practical example used is shown to be problematic. The various prior distributions are shown to be unreasonable in terms of what they imply about the joint distribution of the overall treatment effect and the random effects variance. An alternative form of prior distribution is tentatively proposed. Finally, some practical recommendations are made that stress the value both of fixed effect analyses and of frequentist approaches as well as various diagnostic investigations.
Abstract Good random number generators are (not so) easy to find
"... Every random number generator has its advantages and deficiencies. There are no ``safe' ' generators. The practitioner's problem is how to decide which random number generator will suit his needs best. In this paper, we will discuss criteria for good random number generators: theoreti ..."
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Every random number generator has its advantages and deficiencies. There are no ``safe' ' generators. The practitioner's problem is how to decide which random number generator will suit his needs best. In this paper, we will discuss criteria for good random number generators: theoretical support, empirical evidence and practical aspects. We will study several recent algorithms that perform better than most generators in actual use. We will compare the different methods and supply numerical results as well as selected pointers and links to important literature and other sources. Additional information on random number generation, including the code of most algorithms discussed in this paper is available from our web-server under the
