/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

http://glaros.dtc.umn.edu/gkhome/fetch/papers/mlJPDC98.pdf

Multilevelk-way Partitioning Scheme for Irregular Graphs

Numerical Initial Value Problems in Ordinary Differential Equations

A new method for detecting spikes in acoustic Doppler velocimeter data sequences is suggested. The method combines three concepts: (1) that differentiation enhances the high frequency portion of a signal, (2) that the expected maximum of a random series is given by the Universal threshold, and (3) that good data cluster in a dense cloud in phase space or Poincare maps. These concepts are used to construct an ellipsoid in three-dimensional phase space, then points lying outside the ellipsoid are designated as spikes. The new method is shown to have superior performance to various other methods and it has the added advantage that it requires no parameters. Several methods for replacing sequences of spurious data are presented. A polynomial fitted to good data on either side of the spike event, then interpolated across the event, is preferred by the authors.

Despiking Acoustic Doppler Velocimeter Data

We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several others tests proposed in the literature, and some original ones. Predefined tests suites for sequences of uniform random numbers over the interval (0, 1) and for bit sequences are available. Tools are also offered 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. Finally, the library provides various types of generators implemented in generic form, as well as many specific generators proposed in the literature or found in widely used software. The tests can be applied to instances of the generators predefined in the library, or to user-defined generators, or to streams of random numbers produced by any kind of device or stored in files. Besides introducing TestU01, the article provides a survey and a classification of statistical tests for RNGs. It also applies batteries of tests to a long list of widely used RNGs.

/pdf/testu01-a-c-library-for-empirical-testing-of-random-number-23bwi3w09n.pdf

TestU01: A C library for empirical testing of random number generators

In this article we present background, rationale, and a description of the Scalable Parallel Random Number Generators (SPRNG) library. We begin by presenting some methods for parallel pseudorandom number generation. We will focus on methods based on parameterization, meaning that we will not consider splitting methods such as the leap-frog or blocking methods. We describe, in detail, parameterized versions of the following pseudorandom number generators: (i) linear congruential generators, (ii) shift-register generators, and (iii) lagged-Fibonacci generators. We briefly describe the methods, detail some advantages and disadvantages of each method, and recount results from number theory that impact our understanding of their quality in parallel applications. SPRNG was designed around the uniform implementation of different families of parameterized random number generators. We then present a short description of SPRNG. The description contained within this document is meant only to outline the rationale behind and the capabilities of SPRNG. Much more information, including examples and detailed documentation aimed at helping users with putting and using SPRNG on scalable systems is available at htt;//sprng.sc.fsu.edu. In this description of SPRNG we discuss the random-number generator library as well as the suite of tests of randomness that is an integral part of SPRNG. Random-number tools for parallel Monte Carlo applications must be subjected to classical as well as new types of empirical tests of randomness to eliminate generators that show defects when used in scalable envionments.

Algorithm 806: SPRNG: a scalable library for pseudorandom number generation

Monte Carlo computations are considered easy to parallelize. However, the results can be adversely affected by defects in the parallel pseudorandom number generator used. A parallel pseudorandom number generator must be tested for two types of correlations--(i) intra-stream correlation, as for any sequential generator, and (ii) inter-stream correlation for correlations between random number streams on different processes. Since bounds on these correlations are difficult to prove mathematically, large and thorough empirical tests are necessary. Many of the popular pseudorandom number generators in use today were tested when computational power was much lower, and hence they were evaluated with much smaller test sizes.This paper describes several tests of pseudorandom number generators, both statistical and application-based. We show defects in several popular generators. We describe the implementation of these tests in the SPRING [ACM Trans. Math. Software 26 (2000) 436; SPRNG--scalable parallel random number generators. SPRNG 1.0--http://www.ncsa.uiuc.edu/ Apps/SPRNG; SPRNG 2.0--http://sprng.cs.fsu.edu] test suite and also present results for the tests conducted on the SPRNG generators. These generators have passed some of the largest empirical random number tests.

/pdf/testing-parallel-random-number-generators-54zbu10qfv.pdf

Testing parallel random number generators

SPRNG: A Scalable Library for Pseudorandom Number Generation.

On the optimal Halton sequence

High performance computing on a large-scale computational grid is complicated by the heterogeneous computational capabilities of each node, node unavailability, and unreliable network connectivity. Replicating computation on multiple nodes can significantly improve performance by reducing task completion time on a grid's dynamic environment. We develop an analytical model to determine the number of task replicas to meet the performance goals in different computational grid configurations. Furthermore, taking advantage of the statistical nature of grid-based Monte Carlo applications, we extend the computational replication technique to an N-out-of-M scheduling strategy for grid-based Monte Carlo applications, which can potentially form a large category of grid-computing applications. In addition, we establish a corresponding model for the N-out-of-M scheduling mechanism. Simulations are used to validate the computational replication models. Our preliminary results show that the models we use are effective in predicting the required number of replicas to achieve short task completion time with a given high probability.

/pdf/improving-performance-via-computational-replication-on-a-5ekjb8otqv.pdf

Michael Mascagni

Papers

Algorithm 806: SPRNG: a scalable library for pseudorandom number generation

Testing parallel random number generators

SPRNG: A Scalable Library for Pseudorandom Number Generation.

On the optimal Halton sequence

Improving performance via computational replication on a large-scale computational grid