Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bag-of-work model. This paper concentrates on analyzing the characteristics of large-scale Monte Carlo computation for grid computing. Based on these analyses, we improve the efficiency of the subtask-scheduling scheme by implementing and analyzing the “N-out-of-M ” strategy, and develop a Monte Carlo-specific lightweight checkpoint technique, which leads to a performance improvement for Monte Carlo grid computing. Also, we enhance the trustworthiness of Monte Carlo grid-computing 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 high-performance grid-computing infrastructure that is capable of providing trustworthy Monte Carlo computation services. 3 Analysis of Large-scale Grid-based Monte Carlo Applications

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. 1.

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 bag-of-work model. We improve the efficiency of the subtask-scheduling scheme by using an N-out-of-M strategy, and develop a Monte Carlo-specific lightweight checkpoint technique, which leads to a performance improvement for Monte Carlo grid computing. Also, we enhance the trustworthiness of Monte Carlo grid-computing 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 high-performance gridcomputing infrastructure that is capable of providing trustworthy Monte Carlo computation services. 1.

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The implementation of large-scale Monte Carlo computation on the grid benefits from state-of-the-art 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 Grid-Computing Infrastructure for Monte Carlo Applications (GCIMCA). GCIMCA implements services specific to grid-based Monte Carlo applications, including the Monte Carlo subtask schedule service using the N-out-of-M strategy, the facilities of application-level checkpointing, the partial result validation service, and the intermediate value validation service. Based on these facilities, GCIMCA intends to provide a trustworthy grid-computing infrastructure for large-scale and high-performance distributed Monte Carlo computations. 1.

Monte Carlo methods provide enormous scope for realistic statistical modeling and simulation. The implementation of large-scale Monte Carlo applications on the grid benefits from state-of-the-art 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 Grid-Computing 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 N-out-of-M strategy, the facilities of application-level 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 Workflow-based 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 grid-computing environment for large-scale and high-performance distributed Monte Carlo simulation applications. 1 1.

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 built-in 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

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

Abstract. Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed using the dynamic bag-of-work model which is well suited to parallel, distributed, and grid-based architectures. This paper concentrates on providing computational infrastructure for Monte Carlo applications on such architectures. This is accomplished by analyzing the characteristics of large-scale 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 “N-out-of-M ” strategy, and develop a Monte Carlo-specific 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 grid-computing infrastructure that is capable of providing trustworthy Monte Carlo computation services. 1