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Author: Christoph W. Kessler
Publisher: Springer Science & Business Media
ISBN: 3322878651
Category : Computers
Languages : en
Pages : 235
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Book Description
Distributed-memory multiprocessing systems (DMS), such as Intel's hypercubes, the Paragon, Thinking Machine's CM-5, and the Meiko Computing Surface, have rapidly gained user acceptance and promise to deliver the computing power required to solve the grand challenge problems of Science and Engineering. These machines are relatively inexpensive to build, and are potentially scalable to large numbers of processors. However, they are difficult to program: the non-uniformity of the memory which makes local accesses much faster than the transfer of non-local data via message-passing operations implies that the locality of algorithms must be exploited in order to achieve acceptable performance. The management of data, with the twin goals of both spreading the computational workload and minimizing the delays caused when a processor has to wait for non-local data, becomes of paramount importance. When a code is parallelized by hand, the programmer must distribute the program's work and data to the processors which will execute it. One of the common approaches to do so makes use of the regularity of most numerical computations. This is the so-called Single Program Multiple Data (SPMD) or data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data.
Author: Christoph W. Kessler
Publisher: Springer Science & Business Media
ISBN: 3322878651
Category : Computers
Languages : en
Pages : 235
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Book Description
Distributed-memory multiprocessing systems (DMS), such as Intel's hypercubes, the Paragon, Thinking Machine's CM-5, and the Meiko Computing Surface, have rapidly gained user acceptance and promise to deliver the computing power required to solve the grand challenge problems of Science and Engineering. These machines are relatively inexpensive to build, and are potentially scalable to large numbers of processors. However, they are difficult to program: the non-uniformity of the memory which makes local accesses much faster than the transfer of non-local data via message-passing operations implies that the locality of algorithms must be exploited in order to achieve acceptable performance. The management of data, with the twin goals of both spreading the computational workload and minimizing the delays caused when a processor has to wait for non-local data, becomes of paramount importance. When a code is parallelized by hand, the programmer must distribute the program's work and data to the processors which will execute it. One of the common approaches to do so makes use of the regularity of most numerical computations. This is the so-called Single Program Multiple Data (SPMD) or data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data.
Author: Samuel Midkiff
Publisher: Springer Nature
ISBN: 3031017366
Category : Technology & Engineering
Languages : en
Pages : 157
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Book Description
Compiling for parallelism is a longstanding topic of compiler research. This book describes the fundamental principles of compiling "regular" numerical programs for parallelism. We begin with an explanation of analyses that allow a compiler to understand the interaction of data reads and writes in different statements and loop iterations during program execution. These analyses include dependence analysis, use-def analysis and pointer analysis. Next, we describe how the results of these analyses are used to enable transformations that make loops more amenable to parallelization, and discuss transformations that expose parallelism to target shared memory multicore and vector processors. We then discuss some problems that arise when parallelizing programs for execution on distributed memory machines. Finally, we conclude with an overview of solving Diophantine equations and suggestions for further readings in the topics of this book to enable the interested reader to delve deeper into the field. Table of Contents: Introduction and overview / Dependence analysis, dependence graphs and alias analysis / Program parallelization / Transformations to modify and eliminate dependences / Transformation of iterative and recursive constructs / Compiling for distributed memory machines / Solving Diophantine equations / A guide to further reading
Author: Alain Darte
Publisher: Springer Science & Business Media
ISBN: 9780817641498
Category : Computers
Languages : en
Pages : 284
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Book Description
Readership This book is devoted to the study of compiler transformations that are needed to expose the parallelism hiddenin a program. This book is notan introductory book to parallel processing, nor is it an introductory book to parallelizing compilers. Weassume thatreaders are familiar withthebooks High Performance Compilers for Parallel Computingby Wolfe [121] and Super compilers for Parallel and Vector Computers by Zima and Chapman [125], and that they want to know more about scheduling transformations. In this book we describe both task graph scheduling and loop nest scheduling. Taskgraphschedulingaims atexecuting tasks linked by prece dence constraints; it is a run-time activity. Loop nest scheduling aims at ex ecutingstatementinstances linked bydata dependences;it is a compile-time activity. We are mostly interested in loop nestscheduling,butwe also deal with task graph scheduling for two main reasons: (i) Beautiful algorithms and heuristics have been reported in the literature recently; and (ii) Several graphscheduling, like list scheduling, are the basis techniques used in task ofthe loop transformations implemented in loop nest scheduling. As for loop nest scheduling our goal is to capture in a single place the fantastic developments of the last decade or so. Dozens of loop trans formations have been introduced (loop interchange, skewing, fusion, dis tribution, etc.) before a unifying theory emerged. The theory builds upon the pioneering papers of Karp, Miller, and Winograd [65] and of Lam port [75], and it relies on sophisticated mathematical tools (unimodular transformations, parametric integer linear programming, Hermite decom position, Smithdecomposition, etc.).
Author: Craig C. Douglas
Publisher: SIAM
ISBN: 9780898718171
Category : Technology & Engineering
Languages : en
Pages : 153
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Book Description
This compact yet thorough tutorial is the perfect introduction to the basic concepts of solving partial differential equations (PDEs) using parallel numerical methods. In just eight short chapters, the authors provide readers with enough basic knowledge of PDEs, discretization methods, solution techniques, parallel computers, parallel programming, and the run-time behavior of parallel algorithms to allow them to understand, develop, and implement parallel PDE solvers. Examples throughout the book are intentionally kept simple so that the parallelization strategies are not dominated by technical details.
Author: Anton Schüller
Publisher: Springer Science & Business Media
ISBN: 3322865762
Category : Technology & Engineering
Languages : en
Pages : 232
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Book Description
This book contains the main results of the German project POPINDA. It surveys the state of the art of industrial aerodynamic design simulations on parallel systems. POPINDA is an acronym for Portable Parallelization of Industrial Aerodynamic Applications. This project started in late 1993. The research and development work invested in POPINDA corresponds to about 12 scientists working full-time for the three and a half years of the project. POPINDA was funded by the German Federal Ministry for Education, Science, Research and Technology (BMBF). The central goals of POPINDA were to unify and parallelize the block-structured aerodynamic flow codes of the German aircraft industry and to develop new algorithmic approaches to improve the efficiency and robustness of these programs. The philosophy behind these goals is that challenging and important numerical appli cations such as the prediction of the 3D viscous flow around full aircraft in aerodynamic design can only be carried out successfully if the benefits of modern fast numerical solvers and parallel high performance computers are combined. This combination is a "conditio sine qua non" if more complex applications such as aerodynamic design optimization or fluid structure interaction problems have to be solved. When being solved in a standard industrial aerodynamic design process, such more complex applications even require a substantial further reduction of computing times. Parallel and vector computers on the one side and innovative numerical algorithms such as multigrid on the other have enabled impressive improvements in scientific computing in the last 15 years.
Author: Lorenz Huelsbergen
Publisher:
ISBN:
Category : Dynamic programming
Languages : en
Pages : 330
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Book Description
The thesis describes the design and implementation of the first concurrent copying collector that does not require special hardware or operating systems support. The collector relies on the language or compiler to identify all program accesses to mutable data. Measurements of the collector's implementation indicate that it removes all perceptible garbage-collection pauses from a program's execution."
Author: Frederik Rehbach
Publisher: Springer Nature
ISBN: 3031306090
Category : Technology & Engineering
Languages : en
Pages : 123
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Book Description
This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case. Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.
Author: Matthias S. Müller
Publisher: Springer
ISBN: 3642023037
Category : Computers
Languages : en
Pages : 192
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Book Description
Annotation This book constitutes the refereed proceedings of the 5th International Workshop on OpenMP, IWOMP 2009, held in Dresden, Germany in June 2009. The papers are organized in topical sections on performance and applications, runtime environments, tools and benchmarks as well as proposed extensions to OpenMP.
Author: Shahram Latifi
Publisher: Springer
ISBN: 3319770284
Category : Computers
Languages : en
Pages : 775
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Book Description
This volume presents a collection of peer-reviewed, scientific articles from the 15th International Conference on Information Technology – New Generations, held at Las Vegas. The collection addresses critical areas of Machine Learning, Networking and Wireless Communications, Cybersecurity, Data Mining, Software Engineering, High Performance Computing Architectures, Computer Vision, Health, Bioinformatics, and Education.
Author: Dianhui Wang
Publisher: Springer
ISBN: 3642258328
Category : Computers
Languages : en
Pages : 838
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Book Description
This book constitutes the refereed proceedings of the 24th Australasian Joint Conference on Artificial Intelligence, AI 2011, held in Perth, Australia, in December 2011. The 82 revised full papers presented were carefully reviewed and selected from 193 submissions. The papers are organized in topical sections on data mining and knowledge discovery, machine learning, evolutionary computation and optimization, intelligent agent systems, logic and reasoning, vision and graphics, image processing, natural language processing, cognitive modeling and simulation technology, and AI applications.
