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Data Parallel C++
Programming Accelerated Systems Using C++ and SYCL
Taschenbuch von James Reinders (u. a.)
Sprache: Englisch

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Beschreibung
"This book, now in is second edition, is the premier resource to learn SYCL 2020 and is the ONLY book you need to become part of this community." Erik Lindahl, GROMACS and Stockholm University

Learn how to accelerate C++ programs using data parallelism and SYCL.
This open access book enables C++ programmers to be at the forefront of this exciting and important development that is helping to push computing to new levels. This updated second edition is full of practical advice, detailed explanations, and code examples to illustrate key topics.
SYCL enables access to parallel resources in modern accelerated heterogeneous systems. Now, a single C++ application can use any combination of devices¿including GPUs, CPUs, FPGAs, and ASICs¿that are suitable to the problems at hand.

This book teaches data-parallel programming using C++ with SYCL and walks through everything needed to program accelerated systems. The book begins by introducing data parallelism and foundational topics for effective use of SYCL. Later chapters cover advanced topics, including error handling, hardware-specific programming, communication and synchronization, and memory model considerations.

All source code for the examples used in this book is freely available on GitHub. The examples are written in modern SYCL and are regularly updated to ensure compatibility with multiple compilers.
What You Will Learn

Accelerate C++ programs using data-parallel programming
Use SYCL and C++ compilers that support SYCL
Write portable code for accelerators that is vendor and device agnostic
Optimize code to improve performance for specific accelerators
Be poised to benefit as new accelerators appear from many vendors

Who This Book Is For

New data-parallel programming and computer programmers interested in data-parallel programming using C++

This is an open access book.
"This book, now in is second edition, is the premier resource to learn SYCL 2020 and is the ONLY book you need to become part of this community." Erik Lindahl, GROMACS and Stockholm University

Learn how to accelerate C++ programs using data parallelism and SYCL.
This open access book enables C++ programmers to be at the forefront of this exciting and important development that is helping to push computing to new levels. This updated second edition is full of practical advice, detailed explanations, and code examples to illustrate key topics.
SYCL enables access to parallel resources in modern accelerated heterogeneous systems. Now, a single C++ application can use any combination of devices¿including GPUs, CPUs, FPGAs, and ASICs¿that are suitable to the problems at hand.

This book teaches data-parallel programming using C++ with SYCL and walks through everything needed to program accelerated systems. The book begins by introducing data parallelism and foundational topics for effective use of SYCL. Later chapters cover advanced topics, including error handling, hardware-specific programming, communication and synchronization, and memory model considerations.

All source code for the examples used in this book is freely available on GitHub. The examples are written in modern SYCL and are regularly updated to ensure compatibility with multiple compilers.
What You Will Learn

Accelerate C++ programs using data-parallel programming
Use SYCL and C++ compilers that support SYCL
Write portable code for accelerators that is vendor and device agnostic
Optimize code to improve performance for specific accelerators
Be poised to benefit as new accelerators appear from many vendors

Who This Book Is For

New data-parallel programming and computer programmers interested in data-parallel programming using C++

This is an open access book.
Über den Autor

James Reinders is an Engineer at Intel Corporation with more than four decades of experience in parallel computing and is an author/co-author/editor of more than 10 technical books related to parallel programming. He has a passion for system optimization and teaching. He has had the great fortune to help make contributions to three of the world's fastest computers (#1 on the TOP500 list) as well as many other supercomputers and software developer tools.

Ben Ashbaugh is a Software Architect at Intel Corporation, where he has worked for over 20 years developing software drivers and compilers for Intel graphics products. For the past 10 years, he has focused on parallel programming models for general-purpose computation on graphics processors, including SYCL and the DPC++ compiler. He is active in the Khronos SYCL, OpenCL, and SPIR working groups; helping to define industry standards for parallel programming; and he has authored numerous extensions to expose unique Intel GPU features.

James Brodman is a Principal Engineer at Intel Corporation, working on runtimes and compilers for parallel programming, and he is one of the architects of DPC++. He has a PhD in Computer Science from the University of Illinois at Urbana-Champaign.

Michael Kinsner is a Principal Engineer at Intel Corporation, developing parallel programming languages and compilers for a variety of architectures. He contributes extensively to spatial architectures and programming models and is an Intel representative within The Khronos Group where he works on the SYCL and OpenCL industry standards for parallel programming. He has a PhD in Computer Engineering from McMaster University and is passionate about programming models that cross architectures while still enabling performance.

John Pennycook is a Software Enabling and Optimization Architect at Intel Corporation, focused on enabling developers to fully utilize the parallelism available in modern processors. He is experienced in optimizing and parallelizing applications from a range of scientific domains, and previously served as Intel's representative on the steering committee for the Intel eXtreme Performance User's Group (IXPUG). He has a PhD in Computer Science from the University of Warwick. His research interests are varied, but a recurring theme is the ability to achieve application "performance portability" across different hardware architectures.

Xinmin Tian is an Intel Fellow and Compiler Architect at Intel Corporation and serves as Intel's representative on OpenMP Architecture Review Board (ARB). He has been driving OpenMP offloading, vectorization, and parallelization compiler technologies for Intel architectures. His current focus is on LLVM-based OpenMP offloading, SYCL/DPC++ compiler optimizations for CPUs/GPUs, and tuning HPC/AI application performance. He has a PhD in Computer Science from Tsinghua University, holds 27 US patents, has published over 60 technical papers with over 1300+ citations of his work, and has co-authored two books that span his expertise.

Zusammenfassung

Teaches heterogenous programming for CPU, GPU, FPGA, ASIC, etc.

Presents a vision for the future of parallel programming support in C++

Shows you how to program with industrial strength implementations of SYCL, with extensions

This book is open access, which means that you have free and unlimited access.

Inhaltsverzeichnis

Chapter 1: Introduction.- Chapter 2: Where Code Executes.- Chapter 3: Data Management and Ordering the Uses of Data.- Chapter 4: Expressing Parallelism.- Chapter 5: Error Handling.- Chapter 6: Unified Shared Memory.- Chapter 7: Buffers.- Chapter 8: Scheduling Kernels and Data Movement.- Chapter 9: Local Memory and Work-group Barriers.- Chapter 10: Defining Kernels.- Chapter 11: Vector and Math Arrays.- Chapter 12: Device Information and Kernel Specialization.- Chapter 13: Practical Tips.- Chapter 14: Common Parallel Patterns.- Chapter 15: Programming for GPUs.- Chapter 16: Programming for CPUs.- Chapter 17: Programming for FFGAs.- Chapter 18: Libraries.- Chapter 19: Memory Model and Atomics.- Chapter 20: Backend Interoperability.- Chapter 21: Migrating CUDA Code.- Epilogue.

Details
Erscheinungsjahr: 2023
Fachbereich: Programmiersprachen
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 660
Inhalt: xxx
630 S.
35 s/w Illustr.
294 farbige Illustr.
630 p. 329 illus.
294 illus. in color.
ISBN-13: 9781484296905
ISBN-10: 1484296907
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Reinders, James
Ashbaugh, Ben
Tian, Xinmin
Kinsner, Michael
Pennycook, John
Brodman, James
Auflage: 2nd ed.
Hersteller: Apress
Apress L.P.
Maße: 235 x 155 x 36 mm
Von/Mit: James Reinders (u. a.)
Erscheinungsdatum: 04.10.2023
Gewicht: 0,984 kg
preigu-id: 127200910
Über den Autor

James Reinders is an Engineer at Intel Corporation with more than four decades of experience in parallel computing and is an author/co-author/editor of more than 10 technical books related to parallel programming. He has a passion for system optimization and teaching. He has had the great fortune to help make contributions to three of the world's fastest computers (#1 on the TOP500 list) as well as many other supercomputers and software developer tools.

Ben Ashbaugh is a Software Architect at Intel Corporation, where he has worked for over 20 years developing software drivers and compilers for Intel graphics products. For the past 10 years, he has focused on parallel programming models for general-purpose computation on graphics processors, including SYCL and the DPC++ compiler. He is active in the Khronos SYCL, OpenCL, and SPIR working groups; helping to define industry standards for parallel programming; and he has authored numerous extensions to expose unique Intel GPU features.

James Brodman is a Principal Engineer at Intel Corporation, working on runtimes and compilers for parallel programming, and he is one of the architects of DPC++. He has a PhD in Computer Science from the University of Illinois at Urbana-Champaign.

Michael Kinsner is a Principal Engineer at Intel Corporation, developing parallel programming languages and compilers for a variety of architectures. He contributes extensively to spatial architectures and programming models and is an Intel representative within The Khronos Group where he works on the SYCL and OpenCL industry standards for parallel programming. He has a PhD in Computer Engineering from McMaster University and is passionate about programming models that cross architectures while still enabling performance.

John Pennycook is a Software Enabling and Optimization Architect at Intel Corporation, focused on enabling developers to fully utilize the parallelism available in modern processors. He is experienced in optimizing and parallelizing applications from a range of scientific domains, and previously served as Intel's representative on the steering committee for the Intel eXtreme Performance User's Group (IXPUG). He has a PhD in Computer Science from the University of Warwick. His research interests are varied, but a recurring theme is the ability to achieve application "performance portability" across different hardware architectures.

Xinmin Tian is an Intel Fellow and Compiler Architect at Intel Corporation and serves as Intel's representative on OpenMP Architecture Review Board (ARB). He has been driving OpenMP offloading, vectorization, and parallelization compiler technologies for Intel architectures. His current focus is on LLVM-based OpenMP offloading, SYCL/DPC++ compiler optimizations for CPUs/GPUs, and tuning HPC/AI application performance. He has a PhD in Computer Science from Tsinghua University, holds 27 US patents, has published over 60 technical papers with over 1300+ citations of his work, and has co-authored two books that span his expertise.

Zusammenfassung

Teaches heterogenous programming for CPU, GPU, FPGA, ASIC, etc.

Presents a vision for the future of parallel programming support in C++

Shows you how to program with industrial strength implementations of SYCL, with extensions

This book is open access, which means that you have free and unlimited access.

Inhaltsverzeichnis

Chapter 1: Introduction.- Chapter 2: Where Code Executes.- Chapter 3: Data Management and Ordering the Uses of Data.- Chapter 4: Expressing Parallelism.- Chapter 5: Error Handling.- Chapter 6: Unified Shared Memory.- Chapter 7: Buffers.- Chapter 8: Scheduling Kernels and Data Movement.- Chapter 9: Local Memory and Work-group Barriers.- Chapter 10: Defining Kernels.- Chapter 11: Vector and Math Arrays.- Chapter 12: Device Information and Kernel Specialization.- Chapter 13: Practical Tips.- Chapter 14: Common Parallel Patterns.- Chapter 15: Programming for GPUs.- Chapter 16: Programming for CPUs.- Chapter 17: Programming for FFGAs.- Chapter 18: Libraries.- Chapter 19: Memory Model and Atomics.- Chapter 20: Backend Interoperability.- Chapter 21: Migrating CUDA Code.- Epilogue.

Details
Erscheinungsjahr: 2023
Fachbereich: Programmiersprachen
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 660
Inhalt: xxx
630 S.
35 s/w Illustr.
294 farbige Illustr.
630 p. 329 illus.
294 illus. in color.
ISBN-13: 9781484296905
ISBN-10: 1484296907
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Reinders, James
Ashbaugh, Ben
Tian, Xinmin
Kinsner, Michael
Pennycook, John
Brodman, James
Auflage: 2nd ed.
Hersteller: Apress
Apress L.P.
Maße: 235 x 155 x 36 mm
Von/Mit: James Reinders (u. a.)
Erscheinungsdatum: 04.10.2023
Gewicht: 0,984 kg
preigu-id: 127200910
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