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Algorithms and software for stochastic simulation of biochemical reacting systems
, 2007
"... Traditional deterministic approaches for simulation of chemically reacting systems fail to capture the randomness inherent in such systems at scales common in intracellular biochemical processes. In this article we briefly review the state of the art in discrete stochastic and multiscale algorithms ..."
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Cited by 28 (7 self)
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Traditional deterministic approaches for simulation of chemically reacting systems fail to capture the randomness inherent in such systems at scales common in intracellular biochemical processes. In this article we briefly review the state of the art in discrete stochastic and multiscale algorithms for simulation of biochemical systems and we present the StochKit software toolkit.
Efficient parallellization of stochastic simulation algorithm for chemically reacting systems on the graphics processing unit
, 2008
"... Abstract: In biological systems formed by living cells, the small populations of some reactant species can result in inherent randomness which cannot be captured by traditional deterministic approaches. In that case, a more accurate simulation can be obtained by using the Stochastic Simulation Algor ..."
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Cited by 23 (1 self)
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Abstract: In biological systems formed by living cells, the small populations of some reactant species can result in inherent randomness which cannot be captured by traditional deterministic approaches. In that case, a more accurate simulation can be obtained by using the Stochastic Simulation Algorithm (SSA). Many stochastic realizations are required to capture accurate statistical information of the solution. This carries a very high computational cost. The current generation of graphics processing units (GPU) is wellsuited to this task. We describe our implementation, and present some computational experiments illustrating the power of this technology for this important and challenging class of problems.
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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. 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...
Parallel and distributed computing issues in pricing financial derivatives through Quasi Monte Carlo
 IN PROCEEDINGS OF THE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS.02
, 2002
"... Monte Carlo (MC) techniques are often used to price complex financial derivatives. The computational effort can be substantial when high accuracy is required. However, MC computations are latency tolerant, and are thus easy parallelize even with high communication overheads, such as in a distributed ..."
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Cited by 13 (0 self)
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Monte Carlo (MC) techniques are often used to price complex financial derivatives. The computational effort can be substantial when high accuracy is required. However, MC computations are latency tolerant, and are thus easy parallelize even with high communication overheads, such as in a distributed computing environment. A drawback of MC is its relatively slow convergence rate, which can be overcome through the use of Quasi Monte Carlo (QMC) techniques, which use low discrepancy sequences. We discuss the issues that arise in parallelizing QMC, especially in a heterogeneous computing environment, and present results of empirical studies on arithmetic Asian options, using three parallel QMC techniques that have recently been proposed. We expect the conclusions to be valid for other applications too.
Parameterizing parallel multiplicative laggedfibonacci generators
 Parallel Computing
, 2004
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Analysis of largescale gridbased Monte Carlo applications
 Jour. of High Performance Comp. App
, 2003
"... Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bagofwork model. This paper concentrates on analyzing the characteristics of largescale Monte Carlo computation for grid com ..."
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Cited by 12 (9 self)
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Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bagofwork model. This paper concentrates on analyzing the characteristics of largescale Monte Carlo computation for grid computing. Based on these analyses, we improve the efficiency of the subtaskscheduling scheme by implementing and analyzing the “NoutofM ” strategy, and develop a Monte Carlospecific lightweight checkpoint technique, which leads to a performance improvement for Monte Carlo grid computing. Also, we enhance the trustworthiness of Monte Carlo gridcomputing applications by utilizing the statistical nature of Monte Carlo and by cryptographically validating intermediate results utilizing the random number generator already in use in the Monte Carlo application. All these techniques lead to a highperformance gridcomputing infrastructure that is capable of providing trustworthy Monte Carlo computation services. 3 Analysis of Largescale Gridbased Monte Carlo Applications
Gridbased Monte Carlo Application
 Lecture Notes in Computer Science
, 2002
"... Abstract. Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bagofwork model. We improve the efficiency of the subtaskscheduling scheme by using an NoutofM strategy, and de ..."
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Cited by 7 (6 self)
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Abstract. Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bagofwork model. We improve the efficiency of the subtaskscheduling scheme by using an NoutofM strategy, and develop a Monte Carlospecific lightweight checkpoint technique, which leads to a performance improvement for Monte Carlo grid computing. Also, we enhance the trustworthiness of Monte Carlo gridcomputing applications by utilizing the statistical nature of Monte Carlo and by cryptographically validating intermediate results utilizing the random number generator already in use in the Monte Carlo application. All these techniques lead to a highperformance gridcomputing infrastructure that is capable of providing trustworthy Monte Carlo computation services. 1.
DHPC144 JAPARA – A Java Parallel Random Number Generator Library for HighPerformance 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 largescale scientific applications, such as Monte Ca ..."
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Cited by 4 (0 self)
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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 largescale 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 Simulation for a Fish Schooling Model on a GeneralPurpose Graphics Processing Unit
"... We consider an individualbased model for fish schooling which incorporates a tendency for each fish to align its position and orientation with an appropriate average of its neighbors ’ positions and orientations, plus a tendency for each fish to avoid collisions. To accurately determine statistical ..."
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We consider an individualbased model for fish schooling which incorporates a tendency for each fish to align its position and orientation with an appropriate average of its neighbors ’ positions and orientations, plus a tendency for each fish to avoid collisions. To accurately determine statistical properties of the collective motion of fish whose dynamics are described by such a model, many realizations are typically required. This carries a very high computational cost. The current generation of graphics processing units is wellsuited to this task. We describe our implementation, and present computational experiments illustrating the power of this technology for this important and challenging class of problems. 1.
Gridbased Nonequilibrium MultipleTime Scale Molecular Dynamics/Brownian Dynamics Simulations of LigandReceptor Interactions
 in Structured Protein Systems. Proceeding of the First International Workshop on Biomedical Computations on the Grid (BioGrid'03
, 2003
"... In the hybrid Molecular Dynamics (MD)/Brownian Dynamics (BD) algorithm for simulating the longtime, nonequilibrium dynamics of receptorligand interactions, the evaluation of the force autocorrelation function can be computationally costly but fortunately is highly amenable to multimode processing ..."
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Cited by 3 (3 self)
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In the hybrid Molecular Dynamics (MD)/Brownian Dynamics (BD) algorithm for simulating the longtime, nonequilibrium dynamics of receptorligand interactions, the evaluation of the force autocorrelation function can be computationally costly but fortunately is highly amenable to multimode processing methods. In this paper, taking advantage of the computational grid’s largescale computational resources and the nice characteristics of gridbased Monte Carlo applications, we developed a gridbased receptorligand interactions simulation application using the MD/BD algorithm. We expect to provide highperformance and trustworthy computing for analyzing longtime dynamics of proteins and proteinprotein interaction to predict and understand cell signaling processes and small molecule drug efficacies. Our preliminary results showed that our gridbased application could provide a faster and more accurate computation for the force autocorrelation function in our MD/BD simulation than previous parallel implementations. 1.