Openacc For Programmers

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Openacc For Programmers

Author : Guido Juckeland
ISBN : 0134694287
Genre : Computers
File Size : 26.70 MB
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The book provides a detailed overview of a parallel programming approach for massively parallel processors, OpenACC. The book introduces individual feature sets of OpenACC, with exercises and case studies showcasing the usage of the language constructs and also offers valuable insights into writing an efficient OpenACC program. It also explains how OpenACC language constructs are translated in order to achieve application performance. Chapters on parallelization, optimization strategies and best programming practices are also included.
Category: Computers

Parallel Programming With Openacc

Author : Rob Farber
ISBN : 9780124104594
Genre : Computers
File Size : 52.30 MB
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Parallel Programming with OpenACC is a modern, practical guide to implementing dependable computing systems. The book explains how anyone can use OpenACC to quickly ramp-up application performance using high-level code directives called pragmas. The OpenACC directive-based programming model is designed to provide a simple, yet powerful, approach to accelerators without significant programming effort. Author Rob Farber, working with a team of expert contributors, demonstrates how to turn existing applications into portable GPU accelerated programs that demonstrate immediate speedups. The book also helps users get the most from the latest NVIDIA and AMD GPU plus multicore CPU architectures (and soon for Intel® Xeon PhiTM as well). Downloadable example codes provide hands-on OpenACC experience for common problems in scientific, commercial, big-data, and real-time systems. Topics include writing reusable code, asynchronous capabilities, using libraries, multicore clusters, and much more. Each chapter explains how a specific aspect of OpenACC technology fits, how it works, and the pitfalls to avoid. Throughout, the book demonstrates how the use of simple working examples that can be adapted to solve application needs. Presents the simplest way to leverage GPUs to achieve application speedups Shows how OpenACC works, including working examples that can be adapted for application needs Allows readers to download source code and slides from the book's companion web page
Category: Computers

Learn Cuda Programming

Author : Jaegeun Han
ISBN : 9781788991292
Genre : Computers
File Size : 47.67 MB
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Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and Python Key Features Learn parallel programming principles and practices and performance analysis in GPU computing Get to grips with distributed multi GPU programming and other approaches to GPU programming Understand how GPU acceleration in deep learning models can improve their performance Book Description Compute Unified Device Architecture (CUDA) is NVIDIA's GPU computing platform and application programming interface. It's designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning. Learn CUDA Programming will help you learn GPU parallel programming and understand its modern applications. In this book, you'll discover CUDA programming approaches for modern GPU architectures. You'll not only be guided through GPU features, tools, and APIs, you'll also learn how to analyze performance with sample parallel programming algorithms. This book will help you optimize the performance of your apps by giving insights into CUDA programming platforms with various libraries, compiler directives (OpenACC), and other languages. As you progress, you'll learn how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, you'll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this CUDA book, you'll be equipped with the skills you need to integrate the power of GPU computing in your applications. What you will learn Understand general GPU operations and programming patterns in CUDA Uncover the difference between GPU programming and CPU programming Analyze GPU application performance and implement optimization strategies Explore GPU programming, profiling, and debugging tools Grasp parallel programming algorithms and how to implement them Scale GPU-accelerated applications with multi-GPU and multi-nodes Delve into GPU programming platforms with accelerated libraries, Python, and OpenACC Gain insights into deep learning accelerators in CNNs and RNNs using GPUs Who this book is for This beginner-level book is for programmers who want to delve into parallel computing, become part of the high-performance computing community and build modern applications. Basic C and C++ programming experience is assumed. For deep learning enthusiasts, this book covers Python InterOps, DL libraries, and practical examples on performance estimation.
Category: Computers

Programming Massively Parallel Processors

Author : David B. Kirk
ISBN : 9780128119877
Genre : Computers
File Size : 80.30 MB
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Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth. For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing Utilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environments Contains new and updated case studies Includes coverage of newer libraries, such as CuDNN for Deep Learning
Category: Computers

Professional Cuda C Programming

Author : John Cheng
ISBN : 9781118739310
Genre : Computers
File Size : 84.94 MB
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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.
Category: Computers

Languages And Compilers For Parallel Computing

Author : Călin Cașcaval
ISBN : 9783319099675
Genre : Computers
File Size : 81.16 MB
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This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2013, held in Tokyo, Japan, in September 2012. The 20 revised full papers and two keynote papers presented were carefully reviewed and selected from 44 submissions. The focus of the papers is on following topics: parallel programming models, compiler analysis techniques, parallel data structures and parallel execution models, to GPGPU and other heterogeneous execution models, code generation for power efficiency on mobile platforms, and debugging and fault tolerance for parallel systems.
Category: Computers

Accelerator Programming Using Directives

Author : Sunita Chandrasekaran
ISBN : 9783030122744
Genre : Computers
File Size : 46.94 MB
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This book constitutes the refereed post-conference proceedings of the 5th International Workshop on Accelerator Programming Using Directives, WACCPD 2018, held in Dallas, TX, USA, in November 2018. The 6 full papers presented have been carefully reviewed and selected from 12 submissions. The papers share knowledge and experiences to program emerging complex parallel computing systems. They are organized in the following three sections: applications; using openMP; and program evaluation.
Category: Computers

Accelerator Programming Using Directives

Author : Sandra Wienke
ISBN : 9783030499433
Genre : Computers
File Size : 50.79 MB
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This book constitutes the refereed post-conference proceedings of the 6th International Workshop on Accelerator Programming Using Directives, WACCPD 2019, held in Denver, CO, USA, in November 2019. The 7 full papers presented have been carefully reviewed and selected from 13 submissions. The papers share knowledge and experiences to program emerging complex parallel computing systems. They are organized in the following three sections: porting scientific applications to heterogeneous architectures using directives; directive-based programming for math libraries; and performance portability for heterogeneous architectures.
Category: Computers

Directive Based Gpu Programming For Computational Fluid Dynamics

Author :
ISBN : OCLC:1052077627
Genre :
File Size : 25.76 MB
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Highlights: We evaluate the OpenACC directive-based Fortran API for application to CFD codes. Modifications to the data layout in our test code are required for porting to GPU. Performance is measured on NVIDIA and AMD GPUS with speedups from 2 to 3× over a 16-core Xeon server. Methods for optimization and use of OpenACC across multiple accelerators are examined. Restrictions in the API are found that interfere with modern object-oriented Fortran (2003/8). Abstract: Directive-based programming of graphics processing units (GPUs) has recently appeared as a viable alternative to using specialized low-level languages such as CUDA C and OpenCL for general-purpose GPU programming. This technique, which uses "directive" or "pragma" statements to annotate source codes written in traditional high-level languages, is designed to permit a unified code base to serve multiple computational platforms. In this work we analyze the popular OpenACC programming standard, as implemented by the PGI compiler suite, in order to evaluate its utility and performance potential in computational fluid dynamics (CFD) applications. We examine the process of applying the OpenACC Fortran API to a test CFD code that serves as a proxy for a full-scale research code developed at Virginia Tech; this test code is used to asses the performance improvements attainable for our CFD algorithm on common GPU platforms, as well as to determine the modifications that must be made to the original source code in order to run efficiently on the GPU. Performance is measured on several recent GPU architectures from NVIDIA and AMD (using both double and single precision arithmetic) and the accelerator code is benchmarked against a multithreaded CPU version constructed from the same Fortran source code using OpenMP directives. A single NVIDIA Kepler GPU card is found to perform approximately 20× faster than a single CPU core and more than 2× faster than a 16-core Xeon server. An analysis of optimization techniques for OpenACC reveals cases in which manual intervention by the programmer can improve accelerator performance by up to 30% over the default compiler heuristics, although these optimizations are relevant only for specific platforms. Additionally, the use of multiple accelerators with OpenACC is investigated, including an experimental high-level interface for multi-GPU programming that automates scheduling tasks across multiple devices. While the overall performance of the OpenACC code is found to be satisfactory, we also observe some significant limitations and restrictions imposed by the OpenACC API regarding certain useful features of modern Fortran (2003/8); these are sufficient for us to conclude that it would not be practical to apply OpenACC to our full research code at this time due to the amount of refactoring required.
Category:

Cuda Programming

Author : Shane Cook
ISBN : 9780128029114
Genre : Computers
File Size : 80.66 MB
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CUDA Programming: A Developer's Guide to Parallel Computing with GPUs, Second Edition is a fully revised, updated, practical guide that provides a solid foundation for developers learning parallel programming with CUDA. This guide iincludes updates that cover both the Kepler and Maxwell GPUs from NVIDIA, as well as the latest heterogeneous systems from AMD. Suitable for someone without a parallel programming background or previous CUDA experience, as well as those who already have dabbled in GPU programming, the contents range from installation and getting started, to building your own GPU workstation. This revision includes a new chapter on visualizing data, and new content on the latest CUDA features including data caching, shared memory, and dynamic parallelism. Author Shane Cook also covers the latest host systems and changes to the installation process, NVIDIA’s Parallel NSight IDE, and hardware systems that run CUDA applications. The final new chapter looks ahead to future GPU platforms and releases including on-core ARM CPU and NVlink technologies. Provides a solid foundation in how to program GPUs using in CUDA Discusses multiple options such as libraries, OpenCL, OpenACC and other programming languages Explains how to design and optimize code for several generations of GPUs and platforms Covers the latest debugging and profiling tools
Category: Computers

Addressing Software Managed Cache Development Effort In Gpgpus

Author : Ahmad Lashgar
ISBN : OCLC:1199649489
Genre :
File Size : 76.24 MB
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GPU Computing promises very high performance per watt for highly-parallelizable workloads. Nowadays, there are various programming models developed to utilize the computational power of GPGPUs. Low-level programming models provide full control over GPU resources and allow programmers to achieve peak performance of the chip. In contrast, high-level programming models hide GPU-specific programming details and allow programmers to mainly express parallelism. Later, the compiler parses the parallelization notes and translates them to low-level programming models. This saves tremendous development effort and improves productivity, often achieved at the cost of sacrificing performance. In this dissertation, we investigate the limitations of high-level programming models in achieving a performance near to low-level models. Specifically, we study the performance and productivity gap between high-level OpenACC and low-level CUDA programming models and aim at reducing the performance gap, while maintaining the productivity advantages. We start this study by developing our in-house OpenACC compiler. Our compiler, called IPMACC, translates OpenACC for C to CUDA and uses the system compile to generate GPU binaries. We develop various micro-benchmarks to understand GPU structure and implement a more efficient OpenACC compiler. By using IPMACC, we evaluate the performance and productivity gap between a wide set of OpenACC and CUDA kernels. From our findings, we conclude that one of the major reasons behind the big performance gap between OpenACC and CUDA is CUDA's flexibility in exploiting the GPU software-managed cache. Identifying this key benefit in low-level CUDA, we follow three effective paths in utilizing software-managed cache similar to CUDA, but at a lower development effort (e.g. using OpenACC instead). In the first path, we explore the possibility of employing existing OpenACC directives in utilizing software-managed cache. Specifically, the cache directive is devised in OpenACC API standard to allow the use of software-managed cache in GPUs. We introduce an efficient implementation of OpenACC cache directive that performs very close to CUDA. However, we show that the use of the cache directive is limited and the directive may not offer the full-functionality associated with the software-managed cache, as existing in CUDA. In the second path, we build on our observation on the limitations of the cache directive and propose a new OpenACC directive, called the fcw directive, to address the shortcomings of the cache directive, while maintaining OpenACC productivity advantages. We show that the fcw directive overcomes the cache directive limitations and narrows down the performance gap between CUDA and OpenACC significantly. In the third path, we propose fully-automated hardware/software approach, called TELEPORT, for software-managed cache programming. On the software side, TELEPORT statically analyzes CUDA kernels and identifies opportunities in utilizing the software-managed cache. The required information is passed to the GPU via API calls. Based on this information, on the hardware side, TELEPORT prefetches the data to the software-managed cache at runtime. We show that TELEPORT can improve performance by 32% on average, while lowering the development effort by 2.5X, compared to hand-written CUDA equivalent.
Category:

Parallel Programming For Modern High Performance Computing Systems

Author : Pawel Czarnul
ISBN : 9781351385794
Genre : Business & Economics
File Size : 86.87 MB
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In view of the growing presence and popularity of multicore and manycore processors, accelerators, and coprocessors, as well as clusters using such computing devices, the development of efficient parallel applications has become a key challenge to be able to exploit the performance of such systems. This book covers the scope of parallel programming for modern high performance computing systems. It first discusses selected and popular state-of-the-art computing devices and systems available today, These include multicore CPUs, manycore (co)processors, such as Intel Xeon Phi, accelerators, such as GPUs, and clusters, as well as programming models supported on these platforms. It next introduces parallelization through important programming paradigms, such as master-slave, geometric Single Program Multiple Data (SPMD) and divide-and-conquer. The practical and useful elements of the most popular and important APIs for programming parallel HPC systems are discussed, including MPI, OpenMP, Pthreads, CUDA, OpenCL, and OpenACC. It also demonstrates, through selected code listings, how selected APIs can be used to implement important programming paradigms. Furthermore, it shows how the codes can be compiled and executed in a Linux environment. The book also presents hybrid codes that integrate selected APIs for potentially multi-level parallelization and utilization of heterogeneous resources, and it shows how to use modern elements of these APIs. Selected optimization techniques are also included, such as overlapping communication and computations implemented using various APIs. Features: Discusses the popular and currently available computing devices and cluster systems Includes typical paradigms used in parallel programs Explores popular APIs for programming parallel applications Provides code templates that can be used for implementation of paradigms Provides hybrid code examples allowing multi-level parallelization Covers the optimization of parallel programs
Category: Business & Economics

Creating A Portable Programming Abstraction For Wavefront Patterns Targeting Hpc Systems

Author : Robert Searles
ISBN : 1085557030
Genre :
File Size : 28.47 MB
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This thesis examines existing state-of-the-art wavefront applications and parallelization strategies, which it uses to create a high-level abstraction of wavefront parallelism and a programming language extension that facilitates an easy adaptation of such applications in order to expose parallelism on existing and future HPC systems. This thesis presents an open-source tool called Wavebench, which uses wavefront algorithms commonly found in real-world scientific applications to model the performance impact of wavefront parallelism on HPC systems. This thesis also uses the insights gained during the creation of this tool to apply the developed, high-level abstraction to a real-world case study application called Minisweep: a mini-application representative of the main computational kernel in Oak Ridge National Laboratory’s Denovo radiation transport code used for nuclear reactor modeling. The OpenACC implementation of this abstraction running on NVIDIA’s Volta GPU (present in ORNL’s Summit supercomputer) boasts an 85.06x speedup over serial code, which is larger than CUDAs 83.72x speedup over the same serial implementation. This serves as a proof of concept for the viability of the solutions presented by this thesis.
Category:

Supercomputing

Author : Julian M. Kunkel
ISBN : 9783642387500
Genre : Computers
File Size : 59.51 MB
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This book constitutes the refereed proceedings of the 28th International Supercomputing Conference, ISC 2013, held in Leipzig, Germany, in June 2013. The 35 revised full papers presented together were carefully reviewed and selected from 89 submissions. The papers cover the following topics: scalable applications with 50K+ cores; performance improvements in algorithms; accelerators; performance analysis and optimization; library development; administration and management of supercomputers; energy efficiency; parallel I/O; grid and cloud.
Category: Computers

Cuda For Starters

Author : Emily Warren
ISBN : 1540552047
Genre :
File Size : 54.41 MB
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CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing - an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. The CUDA platform is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. Also, CUDA supports programming frameworks such as OpenACC and OpenCL. When it was first introduced by Nvidia, the name CUDA was an acronym for Compute Unified Device Architecture, but Nvidia subsequently dropped the use of the acronym. 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.
Category:

Cuda For Newbies

Author : Dylan Skinner
ISBN : 1540604322
Genre :
File Size : 52.69 MB
Format : PDF, ePub, Docs
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CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing - an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. The CUDA platform is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. Also, CUDA supports programming frameworks such as OpenACC and OpenCL. When it was first introduced by Nvidia, the name CUDA was an acronym for Compute Unified Device Architecture, but Nvidia subsequently dropped the use of the acronym. 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.
Category:

Cuda Essentials

Author : Jon Bargas
ISBN : 1540386007
Genre :
File Size : 23.37 MB
Format : PDF, Kindle
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CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing - an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. The CUDA platform is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. Also, CUDA supports programming frameworks such as OpenACC and OpenCL. When it was first introduced by Nvidia, the name CUDA was an acronym for Compute Unified Device Architecture, but Nvidia subsequently dropped the use of the acronym. 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.
Category: