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31
A fast algorithm for online placement and reorganization of replicated data
 In Proceedings of the 17th International Parallel & Distributed Processing Symposium (IPDPS 2003
, 2003
"... As storage systems scale to thousands of disks, data distribution and load balancing become increasingly important. We present an algorithm for allocating data objects to disks as a system as it grows from a few disks to hundreds or thousands. A client using our algorithm can locate a data object in ..."
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Cited by 40 (8 self)
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As storage systems scale to thousands of disks, data distribution and load balancing become increasingly important. We present an algorithm for allocating data objects to disks as a system as it grows from a few disks to hundreds or thousands. A client using our algorithm can locate a data object in microseconds without consulting a central server or maintaining a full mapping of objects or buckets to disks. Despite requiring little global configuration data, our algorithm is probabilistically optimal in both distributing data evenly and minimizing data movement when new storage is added to the system. Moreover, our algorithm supports weighted allocation and variable levels of object replication, both of which are needed to permit systems to efficiently grow while accommodating new technology. 1
SPRNG: A Scalable Library for Pseudorandom Number Generation
"... 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 conside ..."
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Cited by 38 (6 self)
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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 leapfrog or blocking methods. We describe in detail
parameterized versions of the following pseudorandom number generators: (i) linear congruential
generators, (ii) shiftregister generators, and (iii) laggedFibonacci generators. We briey 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 dierent 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 the URL:
http://sprng.cs.fsu.edu/RNG. 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 ran
domness to eliminate generators that show defects when used in scalable environments.
Testing Parallel Random Number Generators
"... . A parallel random number generator (PRNG) must be tested for two types of correlations  (i) Intrastream correlation, as for any serial generator, and (ii) Interstream correlation for correlations between random number streams on different processes. Since bounds on these correlations are diffi ..."
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Cited by 21 (1 self)
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. A parallel random number generator (PRNG) must be tested for two types of correlations  (i) Intrastream correlation, as for any serial generator, and (ii) Interstream correlation for correlations between random number streams on different processes. Since bounds on these correlations are difficult to prove mathematically, large empirical tests are necessary. Many of the popular RNGs in use today were tested when computational power was much lower, and hence they were evaluated with much smaller. This paper describes several tests of PRNGs, both statistical and physicallybased tests. We show defects in several popular generators. We then present the results for the tests conducted on the SPRNG generators. These generators have passed some of the largest empirical random number tests ever undertaken. 1 Introduction Monte Carlo (MC) computations have, currently do, and will continue to consume a large fraction of all available highperformance computing cycles. MC methods can be de...
Random Number Generators for Parallel Applications
 in Monte Carlo Methods in Chemical Physics
, 1998
"... this article is devoted, because these com1 putations require the highest quality of random numbers. The ability to do a multidimensional integral relies on properties of uniformity of ntuples of random numbers and/or the equivalent property that random numbers be uncorrelated. The quality aspect i ..."
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Cited by 18 (7 self)
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this article is devoted, because these com1 putations require the highest quality of random numbers. The ability to do a multidimensional integral relies on properties of uniformity of ntuples of random numbers and/or the equivalent property that random numbers be uncorrelated. The quality aspect in the other uses is normally less important simply because the models are usually not all that precisely specified. The largest uncertainties are typically due more to approximations arising in the formulation of the model than those caused by lack of randomness in the random number generator. In contrast, the first class of applications can require very precise solutions. Increasingly, computers are being used to solve very welldefined but hard mathematical problems. For example, as Dirac [1] observed in 1929, the physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are completely known and it is only necessary to find precise methods for solving the equations for complex systems. In the intervening years fast computers and new computational methods have come into existence. In quantum chemistry, physical properties must be calculated to "chemical accuracy" (say 0.001 Rydbergs) to be relevant to physical properties. This often requires a relative accuracy of 10
Random Number Generators with Period Divisible by a Mersenne Prime
 Proc. ICCSA 2003
, 2003
"... Pseudorandom numbers with long periods and good statistical properties are often required for applications in computational finance. We consider the requirements for good uniform random number generators, and describe a class of generators whose period is a Mersenne prime or a small multiple of ..."
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Cited by 16 (5 self)
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Pseudorandom numbers with long periods and good statistical properties are often required for applications in computational finance. We consider the requirements for good uniform random number generators, and describe a class of generators whose period is a Mersenne prime or a small multiple of a Mersenne prime. These generators are based on "almost primitive" trinomials, that is trinomials having a large primitive factor. They enable very fast vector/parallel implementations with excellent statistical properties.
Deterministic Parallel RandomNumber Generation for DynamicMultithreading Platforms
"... Existing concurrency platforms for dynamic multithreading do not provide repeatable parallel randomnumber generators. This paper proposes that a mechanism called pedigrees be built into the runtime system to enable efficient deterministic parallel randomnumber generation. Experiments with the open ..."
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Cited by 12 (0 self)
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Existing concurrency platforms for dynamic multithreading do not provide repeatable parallel randomnumber generators. This paper proposes that a mechanism called pedigrees be built into the runtime system to enable efficient deterministic parallel randomnumber generation. Experiments with the opensource MIT Cilk runtime system show that the overhead for maintaining pedigrees is negligible. Specifically, on a suite of 10 benchmarks, the relative overhead of Cilk with pedigrees to the original Cilk has a geometric mean of less than 1%. We persuaded Intel to modify its commercial C/C++ compiler, which provides the Cilk Plus concurrency platform, to include pedigrees, and we built a library implementation of a deterministic parallel randomnumber generator called DOTMIX that compresses the pedigree and then “RC6mixes ” the result. The statistical quality
Parallel Implementation of Stochastic Simulation for Largescale Cellular Processes
 In: Proceedings of the Eighth International Conference on HighPerformance Computing in Asia–Pacific Region (HPCASIA’05
, 2005
"... Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years sto ..."
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Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogenactivated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of largescale genetic regulatory networks and cellular processes. 1.
Fast and reliable random number generators for scientific computing
 PROC. PARA'04 WORKSHOP ON THE STATEOFTHEART INSCIENTIFIC COMPUTING
, 2004
"... Fast and reliable pseudorandom number generators are required for simulation and other applications in Scientific Computing. We outline the requirements for good uniform random number generators, and describe a class of generators having very fast vector/parallel implementations with excellent sta ..."
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Cited by 6 (2 self)
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Fast and reliable pseudorandom number generators are required for simulation and other applications in Scientific Computing. We outline the requirements for good uniform random number generators, and describe a class of generators having very fast vector/parallel implementations with excellent statistical properties. We also discuss the problem of initialising random number generators, and consider how to combine two or more generators to give a better (though usually slower) generator.
Parallel data generation for performance analysis of large, complex RDBMS
 In DBTest ’11
, 2011
"... The exponential growth in the amount of data retained by today’s systems is fostered by a recent paradigm shift towards cloud computing and the vast deployment of datahungry applications, such as social media sites. At the same time systems are capturing more sophisticated data. Running realistic b ..."
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Cited by 5 (3 self)
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The exponential growth in the amount of data retained by today’s systems is fostered by a recent paradigm shift towards cloud computing and the vast deployment of datahungry applications, such as social media sites. At the same time systems are capturing more sophisticated data. Running realistic benchmarks to test the performance and robustness of these applications is becoming increasingly difficult, because of the amount of data that needs to be generated, the number of systems that need to generate the data and the complex structure of the data. These three reasons are intrinsically connected. Whenever large amounts of data are needed, its generation process needs to be highly parallel, in many cases acrosssystems. Since the structure of the data is becoming more and more complex, its parallel generation is extremely challenging. Over the years there have been many papers about data generators, but there has not been a comprehensive overview of the requirements of today’s data generators covering the most complex problems to be solved. In this paper we present such an overview by analyzing the requirements of today’s data generators and either explaining how the problems have been solved in existing data generators, or showing why the problems have not been solved yet.
Linear and Inversive Pseudorandom Numbers for Parallel and Distributed Simulation
 In Twelfth Workshop on Parallel and Distributed Simultation PADS'98, May 26th  29th
, 1998
"... In this work we discuss the use and possible abuse of linear and inversive pseudorandom numbers (PRNs) in parallel and distributed environments. After an investigation of properties of PRNs which determine how these may be applied in such environments we introduce a software package which provides a ..."
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In this work we discuss the use and possible abuse of linear and inversive pseudorandom numbers (PRNs) in parallel and distributed environments. After an investigation of properties of PRNs which determine how these may be applied in such environments we introduce a software package which provides an unified and easytouse approach to the generating and handling of parallel streams of such PRNs. Experimental results are conducted which describe the features of the software package and compare the performance of two selected types of pseudorandom number generators. 1 Introduction Parallel and distributed simulation of discrete event systems has received significant attention since the proliferation of massively parallel and distributed computing platforms [17]. Besides event processing, state update, statistics collection, and many more tasks, random number generation is an important element of every simulation experiment. Whereas the main objective in the parallel and distributed sim...