Programming Your Gpu With Openmp
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Programming Your Gpu With Openmp
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Author : Tom Deakin
language : en
Publisher: MIT Press
Release Date : 2023-11-07
Programming Your Gpu With Openmp written by Tom Deakin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-07 with Computers categories.
The essential guide for writing portable, parallel programs for GPUs using the OpenMP programming model. Today’s computers are complex, multi-architecture systems: multiple cores in a shared address space, graphics processing units (GPUs), and specialized accelerators. To get the most from these systems, programs must use all these different processors. In Programming Your GPU with OpenMP, Tom Deakin and Timothy Mattson help everyone, from beginners to advanced programmers, learn how to use OpenMP to program a GPU using just a few directives and runtime functions. Then programmers can go further to maximize performance by using CPUs and GPUs in parallel—true heterogeneous programming. And since OpenMP is a portable API, the programs will run on almost any system. Programming Your GPU with OpenMP shares best practices for writing performance portable programs. Key features include: The most up-to-date APIs for programming GPUs with OpenMP with concepts that transfer to other approaches for GPU programming. Written in a tutorial style that embraces active learning, so that readers can make immediate use of what they learn via provided source code. Builds the OpenMP GPU Common Core to get programmers to serious production-level GPU programming as fast as possible. Additional features: A reference guide at the end of the book covering all relevant parts of OpenMP 5.2. An online repository containing source code for the example programs from the book—provided in all languages currently supported by OpenMP: C, C++, and Fortran. Tutorial videos and lecture slides.
Multicore And Gpu Programming
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Author : Gerassimos Barlas
language : en
Publisher: Elsevier
Release Date : 2014-12-16
Multicore And Gpu Programming written by Gerassimos Barlas and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-16 with Computers categories.
Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, and CUDA, it teaches the design and development of software capable of taking advantage of today's computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm. Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge with multiple examples, extensive case studies, and full source code. Using this book, you can develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting multicore machines. - Comprehensive coverage of all major multicore programming tools, including threads, OpenMP, MPI, and CUDA - Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance - Particular focus on the emerging area of divisible load theory and its impact on load balancing and distributed systems - Download source code, examples, and instructor support materials on the book's companion website
Professional Cuda C Programming
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Author : John Cheng
language : en
Publisher: John Wiley & Sons
Release Date : 2014-09-09
Professional Cuda C Programming written by John Cheng and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-09 with Computers categories.
Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming. Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including: CUDA Programming Model GPU Execution Model GPU Memory model Streams, Event and Concurrency Multi-GPU Programming CUDA Domain-Specific Libraries Profiling and Performance Tuning The book makes complex CUDA concepts easy to understand for anyone with knowledge of basic software development with exercises designed to be both readable and high-performance. For the professional seeking entrance to parallel computing and the high-performance computing community, Professional CUDA C Programming is an invaluable resource, with the most current information available on the market.
Openmp In A Heterogeneous World
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Author : Barbara Chapman
language : en
Publisher: Springer
Release Date : 2012-05-23
Openmp In A Heterogeneous World written by Barbara Chapman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-23 with Computers categories.
This book constitutes the refereed proceedings of the 8th International Workshop on OpenMP, held in in Rome, Italy, in June 2012. The 18 technical full papers presented together with 7 posters were carefully reviewed and selected from 30 submissions. The papers are organized in topical sections on proposed extensions to OpenMP, runtime environments, optimization and accelerators, task parallelism, validations and benchmarks
A Strategy For Mapping Threads To Gpus In A Directive Based Programming Model
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Author : Chen Shen
language : en
Publisher:
Release Date : 2017
A Strategy For Mapping Threads To Gpus In A Directive Based Programming Model written by Chen Shen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Computer science categories.
The proliferation of accelerators in modern clusters makes efficient coprocessor programming a key requirement if application codes are to achieve high levels of performance with acceptable energy consumption on such platforms. This has led to considerable effort to provide suitable programming models for these accelerators, especially within the OpenMP community. While OpenMP 4.5 offers a rich set of directives, clauses and runtime calls to fully utilize accelerators, an efficient implementation of OpenMP 4.5 for GPUs remains a non-trivial task, given their multiple levels of thread parallelism. In this thesis, we describe a new implementation of the corresponding features of OpenMP 4.5 for GPUs based on a one-to-one mapping of its loop hierarchy parallelism to the GPU thread hierarchy. We assess the impact of this mapping, in particular the use of GPU warps to handle innermost loop execution, on the performance of GPU execution via a set of benchmarks that include a version of the NAS parallel benchmarks specifically developed for this research; we also used the Matrix- Matrix multiplication, Jacobi, Gauss and Laplacian kernels for better understanding the potential performance issues.
Openmp In A Modern World From Multi Device Support To Meta Programming
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Author : Michael Klemm
language : en
Publisher: Springer Nature
Release Date : 2022-09-20
Openmp In A Modern World From Multi Device Support To Meta Programming written by Michael Klemm and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-20 with Computers categories.
This book constitutes the proceedings of the 18th International Workshop on OpenMP, IWOMP 2022, held in Chattanooga, TN, USA, in September 2022. The 11 full papers presented in this volume were carefully reviewed and selected for inclusion in this book from the 13 submissions. The papers are organized in topical sections named: OpenMP and multiple nodes; exploring new and recent OpenMP extensions; effectie use of advanced heterogeneous node architectures; OpenMP tool support; OpenMP and multiple translation units. Chapter "Improving Tool Support for Nested Parallel Regions with Introspection Consistency" is publshed Open Access and licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Compiler And Runtime Based Parallelization Optimization For Gpus
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Author : Guray Ozen
language : en
Publisher:
Release Date : 2018
Compiler And Runtime Based Parallelization Optimization For Gpus written by Guray Ozen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Graphics Processing Units (GPU) have been widely adopted to accelerate the execution of HPC workloads due to their vast computational throughput, ability to execute a large number of threads inside SIMD groups in parallel and their use of hardware multithreading to hide long pipelining and memory access latencies. There are two APIs commonly used for native GPU programming: CUDA, which only targets NVIDIA GPUs and OpenCL, which targets all types of GPUs as well as other accelerators. However these APIs only expose low-level hardware characteristics to the programmer. So developing applications able to exploit the dazzling performance of GPUs is not a trivial task, and becomes even harder when they have irregular data access patterns or control flows. Several approaches have been proposed to help simplify accelerator programming. Models like OpenACC and OpenMP are intended to solve the aforementioned programming challenges. They take a directive based approach which allows the users to insert non-executable directives that guide the compiler to handle the low-level complexities of the system. However they have a performance gap with native programming models as their compiler does not have comprehensive knowledge about how to transform code and what to optimize. This thesis targets directive-based programming models to enhance their capabilities for GPU programming. The thesis introduces a new dialect model, which is a combination of OpenMP and OmpSs. It also includes several extensions and the MACC infrastructure, a source-to-source compiler targeting CUDA developed on top of BSC's Mercurium compiler and able to support the new dialect model. The new model allows the use of multiple GPUs in conjunction with the vector and heavily multithreaded capabilities in multicore processors automatically. Moreover, it introduces new clauses to make use of on-chip memory efficiently. Secondly the thesis focusses on code transformation techniques and proposes the LazyNP method to support nested parallelism for irregular applications such as sparse matrix operations, graph and graphics algorithms. The method efficiently increases thread granularity for the code region where nested parallelism is desired. The compiler generates code to dynamically pack kernel invocations and to postpone their execution until a bunch of them are available. To the best of our knowledge, LazyNP code transformation was the first successful code transformation method related to nested directives for GPUs. Finally, the thesis conducts a thorough exploration of conventional loop scheduling methods on GPUs to find the advantage and disadvantages of each method. It then proposes the concept of optimized dynamic loop scheduling as an improvement to all the existing methods. The contributions of this thesis improve the programmability of GPUs. This has had an outstanding impact on the whole OpenMP and OpenACC language committee. Additionally, our work includes contributions to widely used compilers such as Mercurium, Clang and PGI, helping thousands of users to take advantage of our work.
Cuda By Example
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Author : Jason Sanders
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-07-05
Cuda By Example written by Jason Sanders and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-05 with categories.
GPUs can be used for much more than graphics processing. As opposed to a CPU, which can only run four or five threads at once, a GPU is made up of hundreds or even thousands of individual, low-powered cores, allowing it to perform thousands of concurrent operations. Because of this, GPUs can tackle large, complex problems on a much shorter time scale than CPUs. Dive into parallel programming on NVIDIA hardware with CUDA by Chris Rose, and learn the basics of unlocking your graphics card. This updated and expanded second edition of Book provides a user-friendly introduction to the subject, Taking a clear structural framework, it guides the reader through the subject's core elements. A flowing writing style combines with the use of illustrations and diagrams throughout the text to ensure the reader understands even the most complex of concepts. This succinct and enlightening overview is a required reading for all those interested in the subject . We hope you find this book useful in shaping your future career & Business.
Hands On Gpu Programming With Python And Cuda
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Author : Dr. Brian Tuomanen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-27
Hands On Gpu Programming With Python And Cuda written by Dr. Brian Tuomanen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.
Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook Description Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing. What you will learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho this book is for Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.
Openmp Enabling Massive Node Level Parallelism
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Author : Simon McIntosh-Smith
language : en
Publisher: Springer Nature
Release Date : 2021-09-08
Openmp Enabling Massive Node Level Parallelism written by Simon McIntosh-Smith and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-08 with Computers categories.
This book constitutes the proceedings of the 17th International Workshop on OpenMP, IWOMP 2021, held virtually in September 2021 and hosted by the High Performance Computing research group at the University of Bristol, UK. The 15 full papers presented in this volume were carefully reviewed and selected for inclusion in this book. The papers are organized in topical sections named: synchronization and data; tasking expansions; applications; case studies; and heterogenous computing and memory. Chapter ‘FOTV: A Generic Device Offloading Framework for OpenMP’ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.