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The era of practical parallel programming has arrived, marked by the popularity of the MPI and OpenMP software standards and the emergence of commodity clusters as the hardware platform of choice for an increasing number of organizations. This exciting new book,Parallel Programming in C with MPI and OpenMPaddresses the needs of students and professionals who want to learn how to design, analyze, implement, and benchmark parallel programs in C using MPI and/or OpenMP. It introduces a rock-solid design methodology with coverage of the most important MPI functions and OpenMP directives. It also demonstrates, through a wide range of examples, how to develop parallel programs that will execute efficiently on today’s parallel platforms. If you are an instructor who has adopted the book and would like access to the additional resources, please contact your local sales rep. or Michelle Flomenhoft at: [email protected].
Learn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand. This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems. What You'll Learn Accelerate C++ programs using data-parallel programming Target multiple device types (e.g. CPU, GPU, FPGA) Use SYCL and SYCL compilers Connect with computing’s heterogeneous future via Intel’s oneAPI initiative Who This Book Is For Those new data-parallel programming and computer programmers interested in data-parallel programming using C++.
Foreword by Bjarne Stroustrup Software is generally acknowledged to be the single greatest obstacle preventing mainstream adoption of massively-parallel computing. While sequential applications are routinely ported to platforms ranging from PCs to mainframes, most parallel programs only ever run on one type of machine. One reason for this is that most parallel programming systems have failed to insulate their users from the architectures of the machines on which they have run. Those that have been platform-independent have usually also had poor performance. Many researchers now believe that object-oriented languages may offer a solution. By hiding the architecture-specific constructs required for high performance inside platform-independent abstractions, parallel object-oriented programming systems may be able to combine the speed of massively-parallel computing with the comfort of sequential programming. Parallel Programming Using C++ describes fifteen parallel programming systems based on C++, the most popular object-oriented language of today. These systems cover the whole spectrum of parallel programming paradigms, from data parallelism through dataflow and distributed shared memory to message-passing control parallelism. For the parallel programming community, a common parallel application is discussed in each chapter, as part of the description of the system itself. By comparing the implementations of the polygon overlay problem in each system, the reader can get a better sense of their expressiveness and functionality for a common problem. For the systems community, the chapters contain a discussion of the implementation of the various compilers and runtime systems. In addition to discussing the performance of polygon overlay, several of the contributors also discuss the performance of other, more substantial, applications. For the research community, the contributors discuss the motivations for and philosophy of their systems. As well, many of the chapters include critiques that complete the research arc by pointing out possible future research directions. Finally, for the object-oriented community, there are many examples of how encapsulation, inheritance, and polymorphism can be used to control the complexity of developing, debugging, and tuning parallel software.
A clear illustration of how parallel computers can be successfully appliedto large-scale scientific computations. This book demonstrates how avariety of applications in physics, biology, mathematics and other scienceswere implemented on real parallel computers to produce new scientificresults. It investigates issues of fine-grained parallelism relevant forfuture supercomputers with particular emphasis on hypercube architecture. The authors describe how they used an experimental approach to configuredifferent massively parallel machines, design and implement basic systemsoftware, and develop algorithms for frequently used mathematicalcomputations. They also devise performance models, measure the performancecharacteristics of several computers, and create a high-performancecomputing facility based exclusively on parallel computers. By addressingall issues involved in scientific problem solving, Parallel ComputingWorks! provides valuable insight into computational science for large-scaleparallel architectures. For those in the sciences, the findings reveal theusefulness of an important experimental tool. Anyone in supercomputing andrelated computational fields will gain a new perspective on the potentialcontributions of parallelism. Includes over 30 full-color illustrations.
Numerical algorithms, modern programming techniques, and parallel computing are often taught serially across different courses and different textbooks. The need to integrate concepts and tools usually comes only in employment or in research - after the courses are concluded - forcing the student to synthesise what is perceived to be three independent subfields into one. This book provides a seamless approach to stimulate the student simultaneously through the eyes of multiple disciplines, leading to enhanced understanding of scientific computing as a whole. The book includes both basic as well as advanced topics and places equal emphasis on the discretization of partial differential equations and on solvers. Some of the advanced topics include wavelets, high-order methods, non-symmetric systems, and parallelization of sparse systems. The material covered is suited to students from engineering, computer science, physics and mathematics.
Expert guidance for those programming today’s dual-core processors PCs As PC processors explode from one or two to now eight processors, there is an urgent need for programmers to master concurrent programming. This book dives deep into the latest technologies available to programmers for creating professional parallel applications using C#, .NET 4, and Visual Studio 2010. The book covers task-based programming, coordination data structures, PLINQ, thread pools, asynchronous programming model, and more. It also teaches other parallel programming techniques, such as SIMD and vectorization. Teaches programmers professional-level, task-based, parallel programming with C#, .NET 4, and Visual Studio 2010 Covers concurrent collections, coordinated data structures, PLINQ, thread pools, asynchronous programming model, Visual Studio 2010 debugging, and parallel testing and tuning Explores vectorization, SIMD instructions, and additional parallel libraries Master the tools and technology you need to develop thread-safe concurrent applications for multi-core systems, with Professional Parallel Programming with C#.
Software -- Programming Techniques.
ParCo2007 marks a quarter of a century of the international conferences on parallel computing that started in Berlin in 1983. The aim of the conference is to give an overview of the developments, applications and future trends in high-performance computing for various platforms.
Content addressable memories or content addressable parallel processors or associative memories or associative processors have been around the computing scene for almost 20 years now. Several hundred papers have been written discussing their use and design but so far no single text or reference book has tried to summarize the field. This volume attempts to remedy this lack. It attempts to gather into one place all the information that exists as of late 1975.
This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic n observations, p variables matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming.