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High performance computers.
Software -- Operating Systems.
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++.
This book outlines a set of issues that are critical to all of parallel architecture--communication latency, communication bandwidth, and coordination of cooperative work (across modern designs). It describes the set of techniques available in hardware and in software to address each issues and explore how the various techniques interact.
Software -- Programming Techniques.
Industrial Applications of High-Performance Computing: Best Global Practices offers a global overview of high-performance computing (HPC) for industrial applications, along with a discussion of software challenges, business models, access models (e.g., cloud computing), public-private partnerships, simulation and modeling, visualization, big data a
Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
The contributions of a diverse selection of international hardware and software specialists are assimilated in this book's exploration of the development of massively parallel processing (MPP). The emphasis is placed on industrial applications and collaboration with users and suppliers from within the industrial community consolidates the scope of the publication. From a practical point of view, massively parallel data processing is a vital step to further innovation in all areas where large amounts of data must be processed in parallel or in a distributed manner, e.g. fluid dynamics, meteorology, seismics, molecular engineering, image processing, parallel data base processing. MPP technology can make the speed of computation higher and substantially reduce the computational costs. However, to achieve these features, the MPP software has to be developed further to create user-friendly programming systems and to become transparent for present-day computer software. Application of novel electro-optic components and devices is continuing and will be a key for much more general and powerful architectures. Vanishing of communication hardware limitations will result in the elimination of programming bottlenecks in parallel data processing. Standardization of the functional characteristics of a programming model of massively parallel computers will become established. Then efficient programming environments can be developed. The result will be a widespread use of massively parallel processing systems in many areas of application.
This Festschrift volume includes a collection of papers written in honor of the accomplishments of Professor Yonezawa on the occasion of his 65th birthday in 2012. With a few exceptions, the papers in this Festschrift were presented at an international symposium celebrating this occasion. Also included are reprints of two of Professor Yonezawa's most influential papers on the programming language ABCL. The volume is a testament strong and lasting impact Professor Yonezawa's research accomplishments as well as the inspiration he has been to colleagues and students alike.
By using computer simulations in research and development, computational science and engineering (CSE) allows empirical inquiry where traditional experimentation and methods of inquiry are difficult, inefficient, or prohibitively expensive. The Handbook of Research on Computational Science and Engineering: Theory and Practice is a reference for interested researchers and decision-makers who want a timely introduction to the possibilities in CSE to advance their ongoing research and applications or to discover new resources and cutting edge developments. Rather than reporting results obtained using CSE models, this comprehensive survey captures the architecture of the cross-disciplinary field, explores the long term implications of technology choices, alerts readers to the hurdles facing CSE, and identifies trends in future development.