2014 First Workshop for High Performance Technical Computing in Dynamic Languages

17-17 Nov. 2014

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  • [Title page iii]

    Publication Year: 2014, Page(s): i
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  • [Copyright notice]

    Publication Year: 2014, Page(s): ii
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  • Table of contents

    Publication Year: 2014, Page(s): iii
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  • Message from the HPTCDL'14 Workshop Chairs

    Publication Year: 2014, Page(s): iv
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  • Experimental Multi-threading Support for the Julia Programming Language

    Publication Year: 2014, Page(s):1 - 5
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (307 KB) | HTML iconHTML

    Julia is a young programming language that is designed for technical computing. Although Julia is dynamically typed it is very fast and usually yields C speed by utilizing a just-in-time compiler. Still, Julia has a simple syntax that is similar to Matlab, which is widely known as an easy-to-use programming environment. While Julia is very versatile and provides asynchronous programming facilities... View full abstract»

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  • Petascale Tcl with NAMD, VMD, and Swift/T

    Publication Year: 2014, Page(s):6 - 17
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (203 KB) | HTML iconHTML

    Tcl is the original embeddable dynamic language. Introduced in 1990, Tcl has been the foundation of the scripting interface of the popular biomolecular visualization and analysis program VMD since 1995 and was extended to the parallel molecular dynamics program NAMD in 1999. The two programs together have over 200,000 users who have enjoyed for nearly two decades the stability and flexibility prov... View full abstract»

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  • Convex Optimization in Julia

    Publication Year: 2014, Page(s):18 - 28
    Cited by:  Papers (15)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (342 KB) | HTML iconHTML

    This paper describes Convex<sup>1</sup>, a convex optimization modeling framework in Julia. Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies with the rules of disciplined convex programming (DCP), ... View full abstract»

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  • Parallel Algebraic Modeling for Stochastic Optimization

    Publication Year: 2014, Page(s):29 - 35
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (346 KB) | HTML iconHTML

    We present scalable algebraic modeling software, StochJuMP, for stochastic optimization as applied to power grid economic dispatch. It enables the user to express the problem in a high-level algebraic format with minimal boiler-plate. StochJuMP allows efficient parallel model instantiation across nodes and efficient data localization. Computational results are presented showing that the model cons... View full abstract»

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  • Julia and the Numerical Homogenization of PDEs

    Publication Year: 2014, Page(s):36 - 40
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (249 KB) | HTML iconHTML

    We discuss the advantages of using Julia for solving multiscale problems involving partial differential equations (PDEs). Multiscale problems are problems where the coefficients of a PDE oscillate rapidly on a microscopic length scale, but solutions are sought on a much larger, macroscopic domain. Solving multiscale problems requires both a theoretic result, i.e., a homogenization result yielding ... View full abstract»

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  • Comparing a High and Low-Level Deep Neural Network Implementation for Automatic Speech Recognition

    Publication Year: 2014, Page(s):41 - 46
    Cited by:  Papers (5)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (427 KB) | HTML iconHTML

    The use of deep neural networks (DNNs) has improved performance in several fields including computer vision, natural language processing, and automatic speech recognition (ASR). The increased use of DNNs in recent years has been largely due to performance afforded by GPUs, as the computational cost of training large networks on a CPU is prohibitive. Many training algorithms are well-suited to the ... View full abstract»

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  • Parallel Prefix Polymorphism Permits Parallelization, Presentation &amp;amp; Proof

    Publication Year: 2014, Page(s):47 - 56
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (500 KB) | HTML iconHTML

    Polymorphism in programming languages enables code reuse. Here, we show that polymorphism has broad applicability far beyond computations for technical computing: parallelism in distributed computing, presentation of visualizations of runtime data flow, and proofs for formal verification of correctness. The ability to reuse a single codebase for all these purposes provides new ways to understand a... View full abstract»

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  • A Practical Framework for Infinite-Dimensional Linear Algebra

    Publication Year: 2014, Page(s):57 - 62
    Cited by:  Papers (4)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (344 KB) | HTML iconHTML

    We describe a framework for solving a broad class of infinite-dimensional linear equations, consisting of almost banded operators, which can be used to representing linear ordinary differential equations with general boundary conditions. The framework contains a data structure for on which row operations can be performed, allowing for the solution of infinite-dimensional linear equations by the ad... View full abstract»

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  • Author Index

    Publication Year: 2014, Page(s): 63
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