Results 1  10
of
36
TGFF: Task Graphs for Free
, 1998
"... We present a usercontrollable, generalpurpose, pseudorandom task graph generator called Task Graphs For Free (TGFF). TGFF creates problem instances for use in allocation and scheduling research. It has the ability to generate independent tasks as well as task sets which are composed of partially o ..."
Abstract

Cited by 139 (14 self)
 Add to MetaCart
We present a usercontrollable, generalpurpose, pseudorandom task graph generator called Task Graphs For Free (TGFF). TGFF creates problem instances for use in allocation and scheduling research. It has the ability to generate independent tasks as well as task sets which are composed of partially ordered task graphs. A complete description of a scheduling problem instance is created, including attributes for processors, communication resources, tasks, and intertask communication. The user may parametrically control the correlations between attributes. Sharing TGFF's parameter settings allows researchers to easily reproduce the examples used by others, regardless of the platform on which TGFF is run.
Uniform Random Number Generators for Supercomputers
 Proc. Fifth Australian Supercomputer Conference
, 1992
"... We consider the requirements for uniform pseudorandom number generators on modern vector and parallel supercomputers, consider the pros and cons of various classes of methods, and outline what is currently available. We propose a class of random number generators which have good statistical propert ..."
Abstract

Cited by 26 (11 self)
 Add to MetaCart
We consider the requirements for uniform pseudorandom number generators on modern vector and parallel supercomputers, consider the pros and cons of various classes of methods, and outline what is currently available. We propose a class of random number generators which have good statistical properties and can be implemented efficiently on vector processors and parallel machines. A good method for initialization of these generators is described, and an implementation on a Fujitsu VP 2200/10 vector processor is discussed. 1
Random Number Generators for Parallel Computers
 The NHSE Review
, 1997
"... Random number generators are used in many applications, from slot machines to simulations of nuclear reactors. For many computational science applications, such as Monte Carlo simulation, it is crucial that the generators have good randomness properties. This is particularly true for largescale ..."
Abstract

Cited by 24 (1 self)
 Add to MetaCart
Random number generators are used in many applications, from slot machines to simulations of nuclear reactors. For many computational science applications, such as Monte Carlo simulation, it is crucial that the generators have good randomness properties. This is particularly true for largescale simulations done on highperformance parallel computers. Good random number generators are hard to find, and many widelyused techniques have been shown to be inadequate. Finding highquality, efficient algorithms for random number generation on parallel computers is even more difficult. Here we present a review of the most commonlyused random number generators for parallel computers, and evaluate each generator based on theoretical knowledge and empirical tests. In conclusion, we provide recommendations for using random number generators on parallel computers. Outline This review is organized as follows: A brief summary of the findings of this review is first presented, giving an overview of the use of parallel random number generators and a list of recommended algorithms. Section 1 is an introduction to random number generators and their use in computer simulations on parallel computers. Section 2 is a summary of the methods used to test and evaluate random number generators, on both sequential and parallel computers. Section 3 gives an overview of the main algorithms used to implement random number generators on sequential computers, provides examples of software implementations of the algorithms, and states any known problems with the algorithms or implementations. Section 4 gives a description of the most common methods used to parallelize the sequential algorithms, provides examples of software implementing these algorithms, and states any known problems ...
TestU01: A Software Library in ANSI C for Empirical Testing of Random Number Generators
, 2007
"... This document describes the software library TestU01, implemented in the ANSI C language, and offering a collection of utilities for the (empirical) statistical testing of uniform random number generators (RNG). The library implements several types of generators in generic form, as well as many spec ..."
Abstract

Cited by 18 (2 self)
 Add to MetaCart
This document describes the software library TestU01, implemented in the ANSI C language, and offering a collection of utilities for the (empirical) statistical testing of uniform random number generators (RNG). The library implements several types of generators in generic form, as well as many specific generators proposed in the literature or found in widelyused software. It provides general implementations of the classical statistical tests for random number generators, as well as several others proposed in the literature, and some original ones. These tests can be applied to the generators predefined in the library and to userdefined generators. Specific tests suites for either sequences of uniform random numbers in [0, 1] or bit sequences are also available. Basic tools for plotting vectors of points produced by generators are provided as well. Additional software permits one to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator’s period length, before the generator starts to fail the test systematically.
Techniques for Testing the Quality of Parallel Pseudorandom Number Generators
 in Proc. of the 7th SIAM Conf. on Parallel Processing for Scientific Computing, SIAM
, 1995
"... Ensuring that pseudorandom number generators have good randomness properties is more complicated in a multiprocessor implementationthan in the uniprocessor case. We discuss simple extensions of uniprocessor testing for SIMD parallel streams, and develop in detail a repeatability test for the SPMD pa ..."
Abstract

Cited by 15 (5 self)
 Add to MetaCart
Ensuring that pseudorandom number generators have good randomness properties is more complicated in a multiprocessor implementationthan in the uniprocessor case. We discuss simple extensions of uniprocessor testing for SIMD parallel streams, and develop in detail a repeatability test for the SPMD paradigm. Examples of the application of these tests to an additive laggedFibonacci generator are also given. 1 Introduction Random numbers are used in applications ranging from scientific simulations to statistical sampling procedures. The impracticality in most cases of using true random numbers led to the development of pseudorandom number generators (PRNGs). In the course of this development, testing procedures were designed to ensure that the necessarily deterministic sequence of numbers produced by these PRNGs had analytical and statistical properties which compared well with those of a true random stream. With the tests described by Knuth [1] and Marsaglia [2, 3], an intelligent user ...
RPL2: A Language and Parallel Framework for Evolutionary Computing
 PARALLEL PROBLEM SOLVING FROM NATURE III, LNCS 866
, 1994
"... The Reproductive Plan Language 2 (RPL2) is an extensible interpreted language for writing and using evolutionary computing programs. It supports arbitrary genetic representations, all structured population models described in the literature together with further hybrids, and runs on parallel or s ..."
Abstract

Cited by 13 (7 self)
 Add to MetaCart
The Reproductive Plan Language 2 (RPL2) is an extensible interpreted language for writing and using evolutionary computing programs. It supports arbitrary genetic representations, all structured population models described in the literature together with further hybrids, and runs on parallel or serial hardware while hiding parallelism from the user. This paper surveys structured population models, explains and motivates the benefits of generic systems such as RPL2 and describes the suite of applications that have used it to date.
Orbits and lattices for linear random number generators with composite moduli
 Mathematics of Computation
, 1996
"... Abstract. In order to analyze certain types of combinations of multiple recursive linear congruential generators (MRGs), we introduce a generalized spectral test. We show how to apply the test in large dimensions by a recursive procedure based on the fact that such combinations are subgenerators of ..."
Abstract

Cited by 11 (5 self)
 Add to MetaCart
Abstract. In order to analyze certain types of combinations of multiple recursive linear congruential generators (MRGs), we introduce a generalized spectral test. We show how to apply the test in large dimensions by a recursive procedure based on the fact that such combinations are subgenerators of other MRGs with composite moduli. We illustrate this with the wellknown RANMAR generator. We also design an algorithm generalizing the procedure to arbitrary random number generators. 1.
Catastrophic forgetting and the pseudorehearsal solution in Hopfieldtype networks
 Connection Science
, 1998
"... Pseudorehearsal is a mechanism proposed by Robins which alleviates catastrophic forgetting in multilayer perceptron networks. In this paper, we extend the exploration of pseudorehearsal to a Hop ® eldtype net. The same general principles apply: old information can be rehearsed if it is available, ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
Pseudorehearsal is a mechanism proposed by Robins which alleviates catastrophic forgetting in multilayer perceptron networks. In this paper, we extend the exploration of pseudorehearsal to a Hop ® eldtype net. The same general principles apply: old information can be rehearsed if it is available, and if it is not available, then generating and rehearsing approximations of old information that `map ’ the behaviour of the network can also be eŒective at preser ving the actual old information itself. The details of the pseudorehearsal mechanism, however, bene ® t from being adapted to the dynamics of Hop ® eld nets so as to exploit the extra attractors created in state space during learning. These attractors are usually described as `spurious ’ or `crosstalk’, and regarded as undesirable, interfering with the retention of the trained population items. Our simulations have shown that, in another sense, such attractors can in fact be useful in preser ving the learned population. In general terms, a solution to the catastrophic forgetting problem enables the ongoing or sequential learning of information in arti ® cial neural networks, and consequently also provides a framework for the modelling of lifelong learning/developmental eŒects in cognition.
Multiobjective Synthesis of LowPower RealTime Distributed Embedded Systems
, 2002
"... This dissertation presents methods for automating the synthesis of embedded systems, i.e., specialpurpose computers. In addition, it describes a method for analyzing the manner in which realtime operating system use influences embedded system power consumption. ..."
Abstract

Cited by 9 (2 self)
 Add to MetaCart
This dissertation presents methods for automating the synthesis of embedded systems, i.e., specialpurpose computers. In addition, it describes a method for analyzing the manner in which realtime operating system use influences embedded system power consumption.
Uniform Random Number Generators for Vector and Parallel Computers
 REVISION APPEARED IN PROC. FIFTH AUSTRALIAN SUPERCOMPUTER CONFERENCE
, 1992
"... We consider the requirements for uniform pseudorandom number generators on modern vector and parallel machines; consider the pros and cons of various popular classes of methods and some new methods; and outline what is currently available. We then make a proposal for a class of random number gen ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
We consider the requirements for uniform pseudorandom number generators on modern vector and parallel machines; consider the pros and cons of various popular classes of methods and some new methods; and outline what is currently available. We then make a proposal for a class of random number generators which have good statistical properties and can be implemented efficiently on vector processors and parallel machines. A proposal regarding initialization of these generators is made. We also discuss the results of a trial implementation on a Fujitsu VP 2200/10 vector processor.