Vector Models for Data-parallel Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Vector Models for Data-parallel Computing write by Guy E. Blelloch. This book was released on 1990. Vector Models for Data-parallel Computing available in PDF, EPUB and Kindle. Mathematics of Computing -- Parallelism.
The Data Parallel Programming Model
The Data Parallel Programming Model - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Data Parallel Programming Model write by Guy-Rene Perrin. This book was released on 1996-09-11. The Data Parallel Programming Model available in PDF, EPUB and Kindle. This monograph-like book assembles the thorougly revised and cross-reviewed lectures given at the School on Data Parallelism, held in Les Menuires, France, in May 1996. The book is a unique survey on the current status and future perspectives of the currently very promising and popular data parallel programming model. Much attention is paid to the style of writing and complementary coverage of the relevant issues throughout the 12 chapters. Thus these lecture notes are ideally suited for advanced courses or self-instruction on data parallel programming. Furthermore, the book is indispensable reading for anybody doing research in data parallel programming and related areas.
Programming Models for Parallel Computing
Programming Models for Parallel Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Programming Models for Parallel Computing write by Pavan Balaji. This book was released on 2015-11-06. Programming Models for Parallel Computing available in PDF, EPUB and Kindle. An overview of the most prominent contemporary parallel processing programming models, written in a unique tutorial style. With the coming of the parallel computing era, computer scientists have turned their attention to designing programming models that are suited for high-performance parallel computing and supercomputing systems. Programming parallel systems is complicated by the fact that multiple processing units are simultaneously computing and moving data. This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today. The chapters describe the programming models in a unique tutorial style rather than using the formal approach taken in the research literature. The aim is to cover a wide range of parallel programming models, enabling the reader to understand what each has to offer. The book begins with a description of the Message Passing Interface (MPI), the most common parallel programming model for distributed memory computing. It goes on to cover one-sided communication models, ranging from low-level runtime libraries (GASNet, OpenSHMEM) to high-level programming models (UPC, GA, Chapel); task-oriented programming models (Charm++, ADLB, Scioto, Swift, CnC) that allow users to describe their computation and data units as tasks so that the runtime system can manage computation and data movement as necessary; and parallel programming models intended for on-node parallelism in the context of multicore architecture or attached accelerators (OpenMP, Cilk Plus, TBB, CUDA, OpenCL). The book will be a valuable resource for graduate students, researchers, and any scientist who works with data sets and large computations. Contributors Timothy Armstrong, Michael G. Burke, Ralph Butler, Bradford L. Chamberlain, Sunita Chandrasekaran, Barbara Chapman, Jeff Daily, James Dinan, Deepak Eachempati, Ian T. Foster, William D. Gropp, Paul Hargrove, Wen-mei Hwu, Nikhil Jain, Laxmikant Kale, David Kirk, Kath Knobe, Ariram Krishnamoorthy, Jeffery A. Kuehn, Alexey Kukanov, Charles E. Leiserson, Jonathan Lifflander, Ewing Lusk, Tim Mattson, Bruce Palmer, Steven C. Pieper, Stephen W. Poole, Arch D. Robison, Frank Schlimbach, Rajeev Thakur, Abhinav Vishnu, Justin M. Wozniak, Michael Wilde, Kathy Yelick, Yili Zheng
Vector Layout in Virtual-memory Systems for Data-parallel Computing
Vector Layout in Virtual-memory Systems for Data-parallel Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Vector Layout in Virtual-memory Systems for Data-parallel Computing write by Thomas H. Cormen. This book was released on 1993. Vector Layout in Virtual-memory Systems for Data-parallel Computing available in PDF, EPUB and Kindle.
Data Parallel C++
Data Parallel C++ - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Parallel C++ write by James Reinders. This book was released on 2020-11-19. Data Parallel C++ available in PDF, EPUB and Kindle. 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++.
