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This application note examines how to parallelize a Mandelbrot set generator using Star-P, and explores some techniques which you can use to achieve best performance from your parallel code using Star-P.
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This application note illustrates the analysis of circuit
behavior in the face of process variations using Monte Carlo simulation,
in which each circuit element's parameters are randomly varied over a
range corresponding to the process variations expected when the chip is
manufactured. Running the simulation over many trials, the designer can
gather a statistical view of the robustness of his design.
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Principal component analysis is an important tool for finding statistical dependencies in multidimensional data. It has a wide range of applications, for example in environmental sciences, medical imaging and financial analysis. However, principal component analysis is computer intensive and quickly becomes too time-consuming for serial desktops as data sizes become larger. In this example, Star-P is used to perform the analysis of several data sets in parallel.
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Modern open source high-level languages such as Python and R are increasingly playing an important role in increasing programmer productivity when programming high performance computers. This application note describes our implementation of four of the HPC Challenge Class II benchmarks using the Python interface to Star-P, and demonstrates their performance on clusters of multi-core machines. (This entry was a winner in the 2007 HPC Challenge at the SC07 conference in Reno, NV.)
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The medical/genetics research team at NCI was using a serial MATLAB® program on a desktop to compute cross correlation of measured samples in large data sets. This application note outlines the algorithm, the modifications to the code required to parallelize it, and the performance gains.
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This application note explores an algorithm from the field of astrophysics - the simulation of galactic dynamics, and illustrates the modifications required to parallelize it with Star-P, along with the associated performance gains.
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This application note explores an algorithm from the field of computation photonics, and illustrates the modifications required to parallelize it with Star-P.
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In this application note focused on finite element modeling, we illustrate the steps required to parallelize a structural FEA problem, and associated performance gains. It is interesting to note that FEM problems call for a combination of task-parallel and data-parallel treatment: The task-parallel computing helps speed up the building of the large sparse stiffness matrix and the data-parallel helps us solve the large linear system more efficiently.
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The research team at TU Eindhoven was using a serial MATLAB® program to perform finite element analysis that involved solving a system of linear equations represented by sparse matrices. This application note illustrates the modifications required to parallelize it with Star-P, and resulting gains in performance and ability to work with larger matrices.
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This case study illustrates how Star-P can be applied to a Monte Carlo simulation for Discrete Asian Option pricing.
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With Star-P, a MATLAB® or Python user can easily access numerical libraries from Numerical Algorithms Group, in order to carry out faster and larger parallel computations. This note illustrates the 4-step process.
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This application note explores an image processing algorithm used in pattern matching, and illustrates the modifications required to parallelize it with Star-P, along with the associated performance gains
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This document walks through the steps involved in parallelizing MATLAB® code under Star-P, provides the user with a "bag of tips" for using Star-P's tools to achieve the best performance from your code; and presents several examples of domain-specific code, including Monte Carlo simulation, image processing, interacting bodies and swarms, Mandelbrot set generation.
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The Python language provides comprehensive programming tools and the benefits of interactive programming environment. Star-P's Python client enables users to access high-performance parallel computing resources while preserving the interactive workflow, and leverage much of their existing Python code.
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This guide to Star-P's Python client is intended as a self-guided "test drive." The document introduces the Python user to a handful of parallel programming constructs, and shows several Python algorithms parallelized with Star-P, including Google Page Rank algorithm and Monte Carlo simulation.
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One of the powerful aspects of Python is its open source nature, and availability of a wide array of Python modules for technical computing. These modules can be readily parallelized using the Star-P's task-parallel engine. This application note shows how the open source Python Imaging Library can be run in parallel with Star-P.
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There are several parallel computing modules available for Python users. This application note presents a comparison of Star-P/Python code with an equivalent code based on an open-source parallel Python programming environment.
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ISC Star-P software enables parallel computing in Python, on SMP machines or distributed x86/64 clusters, under work load managers such as PBS Pro. This note provides an illustration of the simple steps needed to make this possible.
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