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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 10 (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.
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 9 (8 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
Parameterizing parallel multiplicative laggedfibonacci generators
 Parallel Computing
, 2004
"... ..."
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.
GCIMCA: A Globus and SPRNG Implementation of a Grid Computing Infrastructure for Monte Carlo Applications
 Proceeding of the International Multiconference in Computer Science and Computer Engineering
, 2003
"... The implementation of largescale Monte Carlo computation on the grid benefits from stateoftheart approaches to accessing a computational grid and requires scalable parallel random number generators with good quality. The Globus software toolkit facilitates the creation and utilization of a compu ..."
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Cited by 2 (2 self)
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The implementation of largescale Monte Carlo computation on the grid benefits from stateoftheart approaches to accessing a computational grid and requires scalable parallel random number generators with good quality. The Globus software toolkit facilitates the creation and utilization of a computational grid for large distributed computational jobs. The Scalable Parallel Random Number Generators (SPRNG) library is designed to generate practically infinite number of random number streams with favorable statistical properties for parallel and distributed Monte Carlo applications. Taking advantage of the facilities of the Globus toolkit and the SPRNG library, we implemented a tool we refer to as the GridComputing Infrastructure for Monte Carlo Applications (GCIMCA). GCIMCA implements services specific to gridbased Monte Carlo applications, including the Monte Carlo subtask schedule service using the NoutofM strategy, the facilities of applicationlevel checkpointing, the partial result validation service, and the intermediate value validation service. Based on these facilities, GCIMCA intends to provide a trustworthy gridcomputing infrastructure for largescale and highperformance distributed Monte Carlo computations. 1.
A Grid WorkflowBased Monte Carlo Simulation Environment
 J. of Neural Parallel and Scientific Comp. (NPSC
, 2004
"... Monte Carlo methods provide enormous scope for realistic statistical modeling and simulation. The implementation of largescale Monte Carlo applications on the grid benefits from stateoftheart approaches to accessing resources in a computational grid. Workflow techniques allow one to describe and ..."
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Cited by 1 (1 self)
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Monte Carlo methods provide enormous scope for realistic statistical modeling and simulation. The implementation of largescale Monte Carlo applications on the grid benefits from stateoftheart approaches to accessing resources in a computational grid. Workflow techniques allow one to describe and enact his simulation processes in a structured, manageable, and verifiable way. We developed the GridComputing Infrastructure for Monte Carlo Applications (GCIMCA) based on the Globus toolkit and the SPRNG library. The Globus toolkit facilitates the creation and utilization of a computational grid for large distributed computational jobs and the Scalable Parallel Random Number Generators (SPRNG) library is designed to generate practically infinite number of random number streams with favorable statistical properties for parallel and distributed Monte Carlo applications. GCIMCA provides grid services specific to gridbased Monte Carlo simulation applications, including the Monte Carlo subtask schedule service using the NoutofM strategy, the facilities of applicationlevel checkpointing, the partial result validation service, and the intermediate value validation service. By taking advantage of emerging grid workflow paradigms and the facilities of GCIMCA, we implemented a Grid Workflowbased Monte Carlo (GWMC) simulation environment. Workflow management services are implemented to manage the Monte Carlo simulation process. Based on these services, we intend to provide a trustworthy and manageable gridcomputing environment for largescale and highperformance distributed Monte Carlo simulation applications. 1 1.
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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Cited by 1 (0 self)
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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 builtin 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
Parallel Pseudorandom Number Generation
, 2005
"... Modern pseudorandom number generators rely on modular arithmetic to generate sequences with good statistical properties. Software packages parallelize these generators using clever parameterization of the generating functions. Unfortunately, these techniques have subtle pitfalls when employed in mas ..."
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Modern pseudorandom number generators rely on modular arithmetic to generate sequences with good statistical properties. Software packages parallelize these generators using clever parameterization of the generating functions. Unfortunately, these techniques have subtle pitfalls when employed in massively parallel environments performing large stochastic simulations. This paper details an alternative method for constructing parallel pseudorandom streams based on simple cryptographic techniques. This new method removes the nuanced difficulties that arise from pseudorandom sequence generator parameterization. Further, the difficulty of finding a reliable generator is explored. 1
Computational Infrastructure for Parallel, Distributed, and GridBased Monte Carlo
"... Abstract. Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed using the dynamic bagofwork model which is well suited to parallel, distributed, and gridbased architectures. This paper concentrates on providi ..."
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Abstract. Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed using the dynamic bagofwork model which is well suited to parallel, distributed, and gridbased architectures. This paper concentrates on providing computational infrastructure for Monte Carlo applications on such architectures. This is accomplished by analyzing the characteristics of largescale Monte Carlo computations, and leveraging the existing Scalable Parallel Random Number Generators (SPRNG) library. Based on these analyses, we improve the efficiency of subtaskscheduling by implementing and analyzing the “NoutofM ” strategy, and develop a Monte Carlospecific lightweight checkpointing technique, which leads to a performance improvement. Also, we enhance the trustworthiness of Monte Carlo applications on these architectures 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
DOI: 10.1007/s1095500544279 Effects of Field Orientation on the Driven Lattice Gas
, 2004
"... Steady states of the driven lattice gas (DLG) on triangular, hexagonal and square lattices with the field at several fixed orientations to the principal lattice vectors were studied by Monte Carlo simulation. In most cases a strong field suppressed change to a lowtemperature ordered phase. On each ..."
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Steady states of the driven lattice gas (DLG) on triangular, hexagonal and square lattices with the field at several fixed orientations to the principal lattice vectors were studied by Monte Carlo simulation. In most cases a strong field suppressed change to a lowtemperature ordered phase. On each lattice, one field orientation that caused nonequilibrium ordering was identified. On triangular and hexagonal lattices, dependence of energy and anisotropy on field strength was studied at those orientations. Anisotropic ordering along the field developed at intermediate temperatures under weak fields. Partial ordering along the field persisted to low temperature under strong fields.