Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
An updated set of basic linear algebra subprograms (BLAS)
ACM Transactions on Mathematical Software (TOMS)
Benchmarking GPUs to tune dense linear algebra
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Acceleration of market value-at-risk estimation
Proceedings of the 2nd Workshop on High Performance Computational Finance
Statistical Timing Analysis: From Basic Principles to State of the Art
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The Monte Carlo methods are an important set of algorithms in computer science. They involve estimating results by statistically sampling a parameter space with a thousands to millions of experiments. The algorithm requires a small set of parameters as input, with which it generates a large amount of computation, and outputs a concise set of aggregated results. The large amount of computation has many independent component with obvious boundaries for parallelization. While the algorithm is well-suited for executing on a highly parallel computing platform, there still exist many challenges such as: selecting a suitable random number generator with the appropriate statistical and computational properties, selecting a suitable distribution conversion method that preserves the statistical properties of the random sequences, leveraging the right abstraction for the computation in the experiments, and designing the an efficient data structures for a particular data working set. This paper presents the Monte Carlo Methods software programming pattern and focuses on the numerical, task, and data perspectives to guide software developers in constructing efficient implementations of applications based on Monte Carlo methods.