Download or read book Performance Analysis and Tuning for General Purpose Graphics Processing Units GPGPU written by Hyesoon Kim and published by Springer Nature. This book was released on 2022-05-31 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: General-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deployment in every computer class from high-end supercomputers to embedded mobile platforms. Relative to more traditional multicore systems of today, GPGPUs have distinctly higher degrees of hardware multithreading (hundreds of hardware thread contexts vs. tens), a return to wide vector units (several tens vs. 1-10), memory architectures that deliver higher peak memory bandwidth (hundreds of gigabytes per second vs. tens), and smaller caches/scratchpad memories (less than 1 megabyte vs. 1-10 megabytes). In this book, we provide a high-level overview of current GPGPU architectures and programming models. We review the principles that are used in previous shared memory parallel platforms, focusing on recent results in both the theory and practice of parallel algorithms, and suggest a connection to GPGPU platforms. We aim to provide hints to architects about understanding algorithm aspect to GPGPU. We also provide detailed performance analysis and guide optimizations from high-level algorithms to low-level instruction level optimizations. As a case study, we use n-body particle simulations known as the fast multipole method (FMM) as an example. We also briefly survey the state-of-the-art in GPU performance analysis tools and techniques. Table of Contents: GPU Design, Programming, and Trends / Performance Principles / From Principles to Practice: Analysis and Tuning / Using Detailed Performance Analysis to Guide Optimization

Download or read book General Purpose Graphics Processor Architectures written by Tor M. Aamodt and published by Springer Nature. This book was released on 2022-05-31 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally developed to support video games, graphics processor units (GPUs) are now increasingly used for general-purpose (non-graphics) applications ranging from machine learning to mining of cryptographic currencies. GPUs can achieve improved performance and efficiency versus central processing units (CPUs) by dedicating a larger fraction of hardware resources to computation. In addition, their general-purpose programmability makes contemporary GPUs appealing to software developers in comparison to domain-specific accelerators. This book provides an introduction to those interested in studying the architecture of GPUs that support general-purpose computing. It collects together information currently only found among a wide range of disparate sources. The authors led development of the GPGPU-Sim simulator widely used in academic research on GPU architectures. The first chapter of this book describes the basic hardware structure of GPUs and provides a brief overview of their history. Chapter 2 provides a summary of GPU programming models relevant to the rest of the book. Chapter 3 explores the architecture of GPU compute cores. Chapter 4 explores the architecture of the GPU memory system. After describing the architecture of existing systems, Chapters 3 and 4 provide an overview of related research. Chapter 5 summarizes cross-cutting research impacting both the compute core and memory system. This book should provide a valuable resource for those wishing to understand the architecture of graphics processor units (GPUs) used for acceleration of general-purpose applications and to those who want to obtain an introduction to the rapidly growing body of research exploring how to improve the architecture of these GPUs.

Download or read book General Purpose Computing On Graphics Processing Units written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2022-07-10 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is General Purpose Computing On Graphics Processing Units The term "general-purpose computing on graphics processing units" (also known as "general-purpose computing on GPUs") refers to the practice of employing a graphics processing unit (GPU), which ordinarily performs computation only for the purpose of computer graphics, to carry out computation in programs that are typically performed by the central processing unit (CPU). The already parallel nature of graphics processing may be further parallelized by using numerous video cards in a single computer or a large number of graphics processors. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: General-purpose computing on graphics processing units Chapter 2: Supercomputer Chapter 3: Flynn's taxonomy Chapter 4: Graphics processing unit Chapter 5: Physics processing unit Chapter 6: Hardware acceleration Chapter 7: Stream processing Chapter 8: BrookGPU Chapter 9: CUDA Chapter 10: Close to Metal Chapter 11: Larrabee (microarchitecture) Chapter 12: AMD FireStream Chapter 13: OpenCL Chapter 14: OptiX Chapter 15: Fermi (microarchitecture) Chapter 16: Pascal (microarchitecture) Chapter 17: Single instruction, multiple threads Chapter 18: Multidimensional DSP with GPU Acceleration Chapter 19: Compute kernel Chapter 20: AI accelerator Chapter 21: ROCm (II) Answering the public top questions about general purpose computing on graphics processing units. (III) Real world examples for the usage of general purpose computing on graphics processing units in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of general purpose computing on graphics processing units' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of general purpose computing on graphics processing units.

Download or read book Performance Analysis and Tuning for General Purpose Graphics Processing Units GPGPU written by Hyesoon Kim and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: General-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deployment in every computer class from high-end supercomputers to embedded mobile platforms. Relative to more traditional multicore systems of today, GPGPUs have distinctly higher degrees of hardware multithreading (hundreds of hardware thread contexts vs. tens), a return to wide vector units (several tens vs. 1-10), memory architectures that deliver higher peak memory bandwidth (hundreds of gigabytes per second vs. tens), and smaller caches/scratchpad memories (less than 1 megabyte vs. 1-10 megabytes). In this book, we provide a high-level overview of current GPGPU architectures and programming models. We review the principles that are used in previous shared memory parallel platforms, focusing on recent results in both the theory and practice of parallel algorithms, and suggest a connection to GPGPU platforms. We aim to provide hints to architects about understanding algorithm aspect to GPGPU. We also provide detailed performance analysis and guide optimizations from high-level algorithms to low-level instruction level optimizations. As a case study, we use n-body particle simulations known as the fast multipole method (FMM) as an example. We also briefly survey the state-of-the-art in GPU performance analysis tools and techniques.

Download or read book Computational Science ICCS 2020 written by Valeria V. Krzhizhanovskaya and published by Springer Nature. This book was released on 2020-06-18 with total page 726 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.

Download or read book General Purpose Computing on Graphics Processing Units for Accelerated Deep Learning in Neural Networks written by Conor Helmick and published by . This book was released on 2022 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphics processing units (GPUs) contain a significant number of cores relative to central processing units (CPUs), allowing them to handle high levels of parallelization in multithreading. A general-purpose GPU (GPGPU) is a GPU that has its threads and memory repurposed on a software level to leverage the multithreading made possible by the GPU’s hardware, and thus is an extremely strong platform for intense computing – there is no hardware difference between GPUs and GPGPUs. Deep learning is one such example of intense computing that is best implemented on a GPGPU, as its hardware structure of a grid of blocks, each containing processing threads, can handle the immense number of necessary calculations in parallel. A convolutional neural network (CNN) created for financial data analysis shows this advantage in the runtime of the training and testing of a neural network.

Download or read book CUDA by Example written by Jason Sanders and published by Addison-Wesley Professional. This book was released on 2010-07-19 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required—just the ability to program in a modestly extended version of C. CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance. Major topics covered include Parallel programming Thread cooperation Constant memory and events Texture memory Graphics interoperability Atomics Streams CUDA C on multiple GPUs Advanced atomics Additional CUDA resources All the CUDA software tools you’ll need are freely available for download from NVIDIA. http://developer.nvidia.com/object/cuda-by-example.html

Download or read book Improving Hardware Multithreading in General Purpose Graphics Processing Units written by Hyun Jin Kim and published by . This book was released on 2017 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: General-purpose graphics processing unit (GPGPU) is one of the most popular many-core accelerators that deliver a massive computing power in parallel applications. GPGPUs mainly rely on the hardware multithreading to hide a short pipeline stall and a long memory latency. Thus, the performance of GPGPU can be signicantly aected by how GPGPU's hardware multithreading is applied. However, nding the optimal hardware multithreading is a complex problem since there are many aspects to be considered. This work studies the mechanisms for improving the eectiveness of hardware multithreading. First, it studies the various scheduling policies and proposes an adaptive scheduling policy that chooses the best scheduling policy at runtime. In addition, it proposes simple but eective warp throttling mechanism that can increase the cache locality. Furthermore, it proposes a hardware prefetching mechanism to extend the memory latency hiding degree of hardware multithreading. Finally, it shows how a limited scalability of the conventional cache miss handling architecture constrains the degree of hardware multithreading and proposes the highly scalable cache miss handling architecture.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book Analyzing Analytics written by Rajesh Bordawekar and published by Springer Nature. This book was released on 2022-05-31 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.

Download or read book Hardware and Software Support for Virtualization written by Edouard Bugnion and published by Springer Nature. This book was released on 2022-06-01 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the core question of the necessary architectural support provided by hardware to efficiently run virtual machines, and of the corresponding design of the hypervisors that run them. Virtualization is still possible when the instruction set architecture lacks such support, but the hypervisor remains more complex and must rely on additional techniques. Despite the focus on architectural support in current architectures, some historical perspective is necessary to appropriately frame the problem. The first half of the book provides the historical perspective of the theoretical framework developed four decades ago by Popek and Goldberg. It also describes earlier systems that enabled virtualization despite the lack of architectural support in hardware. As is often the case, theory defines a necessary—but not sufficient—set of features, and modern architectures are the result of the combination of the theoretical framework with insights derived from practical systems. The second half of the book describes state-of-the-art support for virtualization in both x86-64 and ARM processors. This book includes an in-depth description of the CPU, memory, and I/O virtualization of these two processor architectures, as well as case studies on the Linux/KVM, VMware, and Xen hypervisors. It concludes with a performance comparison of virtualization on current-generation x86- and ARM-based systems across multiple hypervisors.

Download or read book GPGPU Programming for Games and Science written by David H. Eberly and published by CRC Press. This book was released on 2014-08-15 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: An In-Depth, Practical Guide to GPGPU Programming Using Direct3D 11 GPGPU Programming for Games and Science demonstrates how to achieve the following requirements to tackle practical problems in computer science and software engineering: Robustness Accuracy Speed Quality source code that is easily maintained, reusable, and readable The book primarily addresses programming on a graphics processing unit (GPU) while covering some material also relevant to programming on a central processing unit (CPU). It discusses many concepts of general purpose GPU (GPGPU) programming and presents practical examples in game programming and scientific programming. The author first describes numerical issues that arise when computing with floating-point arithmetic, including making trade-offs among robustness, accuracy, and speed. He then shows how single instruction multiple data (SIMD) extensions work on CPUs since GPUs also use SIMD. The core of the book focuses on the GPU from the perspective of Direct3D 11 (D3D11) and the High Level Shading Language (HLSL). This chapter covers drawing 3D objects; vertex, geometry, pixel, and compute shaders; input and output resources for shaders; copying data between CPU and GPU; configuring two or more GPUs to act as one; and IEEE floating-point support on a GPU. The book goes on to explore practical matters of programming a GPU, including code sharing among applications and performing basic tasks on the GPU. Focusing on mathematics, it next discusses vector and matrix algebra, rotations and quaternions, and coordinate systems. The final chapter gives several sample GPGPU applications on relatively advanced topics. Web Resource Available on a supporting website, the author’s fully featured Geometric Tools Engine for computing and graphics saves you from having to write a large amount of infrastructure code necessary for even the simplest of applications involving shader programming. The engine provides robust and accurate source code with SIMD when appropriate and GPU versions of algorithms when possible.

Download or read book Performance and Power Optimization of GPU Architectures for General purpose Computing written by Yue Wang and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The other technique targets on maximizing the average throughput of all parallel processors under the dynamic power constraints. We formalize this target as a linear programming problem and solve it on the runtime. According to the simulation results, the first technique achieves more than 22% power savings with a 4% improvement in performance and the second technique saves 11% power consumption with 9% performance improvement. The contributions of this dissertation represent a significant advancement in the quest for improving performance and reducing energy consumption of GPGPU.

Download or read book Advances in GPU Research and Practice written by Hamid Sarbazi-Azad and published by Morgan Kaufmann. This book was released on 2016-09-15 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in GPU Research and Practice focuses on research and practices in GPU based systems. The topics treated cover a range of issues, ranging from hardware and architectural issues, to high level issues, such as application systems, parallel programming, middleware, and power and energy issues. Divided into six parts, this edited volume provides the latest research on GPU computing. Part I: Architectural Solutions focuses on the architectural topics that improve on performance of GPUs, Part II: System Software discusses OS, compilers, libraries, programming environment, languages, and paradigms that are proposed and analyzed to help and support GPU programmers. Part III: Power and Reliability Issues covers different aspects of energy, power, and reliability concerns in GPUs. Part IV: Performance Analysis illustrates mathematical and analytical techniques to predict different performance metrics in GPUs. Part V: Algorithms presents how to design efficient algorithms and analyze their complexity for GPUs. Part VI: Applications and Related Topics provides use cases and examples of how GPUs are used across many sectors. - Discusses how to maximize power and obtain peak reliability when designing, building, and using GPUs - Covers system software (OS, compilers), programming environments, languages, and paradigms proposed to help and support GPU programmers - Explains how to use mathematical and analytical techniques to predict different performance metrics in GPUs - Illustrates the design of efficient GPU algorithms in areas such as bioinformatics, complex systems, social networks, and cryptography - Provides applications and use case scenarios in several different verticals, including medicine, social sciences, image processing, and telecommunications

Download or read book Performance Analysis and Tuning on Modern CPUs written by and published by Independently Published. This book was released on 2020-11-16 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Performance tuning is becoming more important than it has been for the last 40 years. Read this book to understand your application's performance that runs on a modern CPU and learn how you can improve it. The 170+ page guide combines the knowledge of many optimization experts from different industries.

Download or read book Designing Scientific Applications on GPUs written by Raphael Couturier and published by CRC Press. This book was released on 2013-11-21 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many of today’s complex scientific applications now require a vast amount of computational power. General purpose graphics processing units (GPGPUs) enable researchers in a variety of fields to benefit from the computational power of all the cores available inside graphics cards. Understand the Benefits of Using GPUs for Many Scientific Applications Designing Scientific Applications on GPUs shows you how to use GPUs for applications in diverse scientific fields, from physics and mathematics to computer science. The book explains the methods necessary for designing or porting your scientific application on GPUs. It will improve your knowledge about image processing, numerical applications, methodology to design efficient applications, optimization methods, and much more. Everything You Need to Design/Port Your Scientific Application on GPUs The first part of the book introduces the GPUs and Nvidia’s CUDA programming model, currently the most widespread environment for designing GPU applications. The second part focuses on significant image processing applications on GPUs. The third part presents general methodologies for software development on GPUs and the fourth part describes the use of GPUs for addressing several optimization problems. The fifth part covers many numerical applications, including obstacle problems, fluid simulation, and atomic physics models. The last part illustrates agent-based simulations, pseudorandom number generation, and the solution of large sparse linear systems for integer factorization. Some of the codes presented in the book are available online.

Download or read book Building Search Structures on the GPU written by Shengren Li and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The affordable parallel computing capability provided by the general-purpose graphics processing unit (GPGPU) becomes accessible on most popular computing platforms. Many applications can benefit from redesign for these heterogeneous systems by moving some computation to the GPU. Although the introduction of CUDA and OpenCL makes modern GPU programming much easier than hacking shaders, it is still not trivial to develop GPU programs that fully exploit the capability of the hardware. To further ease software development for the GPU, a lot of well-optimized high-performance parallel primitives that solve common problems have been released. With these reliable building blocks, developers can focus on their custom components and thus become more productive. In this dissertation, we continue this effort and build search structures on the GPU. For the low-dimensional k-approximate nearest neighbors search problem, we implement the shifted sorting algorithm. In each iteration, the data and query points are shifted with a certain amount and then sorted together in Morton code order. Different shift amounts lead to different relative orders among the points; the nearest neighbors of a query in the original space are very likely to have similar Morton codes with the query in some of the sorted lists. We consider a query's 1D nearest neighbors from all the sorted lists as the candidates and select the best k of them as the result. Our approach is independent of the distributions of the data and query points and performs more quickly than the state-of-the-art methods on difficult problem instances. To solve the high-dimensional k-nearest neighbors search problem, we introduce a robust efficient brute-force implementation. A highly optimized matrix multiplication subroutine is modified to calculate the squared Euclidean distances between queries and references. The nearest neighbors selection is accomplished by a parallel truncated merge sort based on the Merge Path algorithm, which partitions merging two sorted lists into several independent small mergings. Compared to other published works, our program is faster and it handles larger inputs. Traditional GPU data structures do not support update operations and have to be rebuilt if the data changes. As the initial attempts to build dynamic data structures on the GPU, we propose two dynamic sorted structures: the GPU search array and the GPU search tree. Both of them support batch insertion, with which we can incrementally build the structures. After an insertion, the structures resume searchability and serve both item queries and range queries using binary search. We describe various implementation strategies for the three operations and illustrate their performance comparisons.