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.

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key FeaturesUnderstand effective synchronization strategies for faster processing using GPUsWrite parallel processing scripts with PyCuda and PyOpenCLLearn to use the CUDA libraries like CuDNN for deep learning on GPUsBook Description GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly. What you will learnUtilize Python libraries and frameworks for GPU accelerationSet up a GPU-enabled programmable machine learning environment on your system with AnacondaDeploy your machine learning system on cloud containers with illustrated examplesExplore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.Perform data mining tasks with machine learning models on GPUsExtend your knowledge of GPU computing in scientific applicationsWho this book is for Data Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.

Graphics processing units (GPUs) are being widely used as co-processors in many domains to accelerate general-purpose workloads that are data-parallel and computationally intensive, i.e., GPGPU. An emerging usage domain is adopting GPGPU to accelerate inherently computationintensive Deep Neural Network (DNN) workloads in autonomous systems. Such autonomous systems are usually time-sensitive, especially for autonomous driving systems. When driving alongside human drivers, loss of life or property may result if the computing systems of the autonomous vehicles fail to respond to events before its deadline. Much research has been conducted to algorithmically optimize the accuracy and performance of deep neural networks, but limited attention has been given to optimizing the execution of GPU-accelerated DNN workloads from the scheduling angle, especially in a time-constrained multi-tasking environment. Adopting GPGPU to accelerate DNN workloads in time-sensitive autonomous systems that are often resource-constrained presents a series of challenges: (1) GPUs are designed to execute nonpreemptively, which may cause priority inversion; (2) How to optimize the execution of GPU-accelerated DNN workloads at the system level in a real-time multi-tasking environment; (3) How to simultaneously achieve two (often) conflicting goals in a resource-constrained embedded CPUGPU heterogeneous platform: timing predictability and energy efficiency, that are essential for any DNN-based autonomous driving system. The goal of the research presented in this dissertation is to solve or remedy the aforementioned challenges. Specifically, we propose GPES, a runtime system that allows GPU executions to be interruptible and preemptable in a multi-tasking environment. We proposed S3 DNN, a systemic solution that optimizes the execution of DNN workloads on GPU in a soft real-time multi-tasking environment. We proposed PredJoule, a runtime system which presents a layer-based approach that controls the timing and optimizes energy efficiency by exploiting each layer's performance/energy characteristics. In addition to the runtime systems we proposed, we investigate the problem of mapping multiple applications implemented using kernel graphs in a heterogeneous system, and present a theoretical framework that formulates this problem as an integer program and a set of practically efficient mapping algorithms. Furthermore we present a reuse-based approach to further improve the predictability of GPU computing.

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.

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.

Faster and more efficient hardware is needed to handle the rapid growth of Big Data processing. Applications such as multimedia, medical analysis, computer vision, and machine learning can be parallelized and accelerated using General-Purpose Computing on Graphics Processing Units (GPGPUs). However, these are power intensive and novel approaches are needed to improve their efficiency. Many of applications also show a tolerance to noise within their computation. Approximate computing is a design strategy in which energy savings and speedup can be achieved at the expense of accuracy. If carefully controlled, many applications can accept small amounts of error and still produce acceptable results. This thesis proposes a number of methods to enable approximate computing for GPUs. We first examine a number of approaches for approximating operations at the core level. Floating point arithmetic, specifically multiplies, make up the majority of instructions computed on GPUs. In this dissertation we propose a configurable floating point unit (CFPU) which eliminates the costly manitassa multiply by copying one of the input mantissa directly to the output. For applications with a higher amount of temporal similiarity we propose adaptive lookup (ALook) to use small dynamic look up tables to store recently computed operations. This low power look up table provides nearest distance matches to provide results rather than computing on the exact hardware. GPUs issue threads in groups, commonly 32, called warps. Cores in a warp run the same instructions in lock-step. Every instruction within a warp must be accelerated to provide performance improvements. To control accuracy, we run the most erroneous approximate results on the exact hardware. Bottlenecks can arise as some threads spend time computing exact results while others use approximate solutions. We propose two methods to handle this problem. First, we use warp pass through to target warps in which a very small fraction of threads must be computed exactly. To handle warps with a larger percentage of exact computations, we utilize warp value trading (WVT). Under WVT, operations are traded between warps running on the same multiprocessor to create uniform groups of either exact or approximate operations. Finally, we focus on application specific approximation. We show approximation can be used to accelerate neural networks during training and inference. Early stages of training tolerate more error than later ones, so we adjust the level of approximation over time. To accelerate inference we approximate larger operations to a lesser degree than larger ones to increase hit rate. For training we show that gradually reducing the maximum allowed error per operation results in 7.13x EDP improvement and 4.64x speedup training of four different neural network applications with less than 2% quality loss. For inference we are able to automatically select parameters based on user prediction requirements for neural networks and improves speedup by 2.9x speedup and EDP by 6.2x of inference across six neural networks.

This book illustrates how to build a GPU parallel computer. If you don't want to waste your time for building, you can buy a built-in-GPU desktop/laptop machine. All you need to do is to install GPU-enabled software for parallel computing. Imagine that we are in the midst of a parallel computing era. The GPU parallel computer is suitable for machine learning, deep (neural network) learning. For example, GeForce GTX1080 Ti is a GPU board with 3584 CUDA cores. Using the GeForce GTX1080 Ti, the performance is roughly 20 times faster than that of an INTEL i7 quad-core CPU. We have benchmarked the MNIST hand-written digits recognition problem (60,000 persons: hand-written digits from 0 to 9). The result of MNIST benchmark for machine learning shows that GPU of a single GeForce GTX1080 Ti board takes only less than 48 seconds while the INTEL i7 quad-core CPU requires 15 minutes and 42 seconds. A CUDA core is most commonly referring to the single-precision floating point units in an SM (streaming multiprocessor). A CUDA core can initiate one single precision floating point instruction per clock cycle. 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. The GPU parallel computer is based on SIMD ( single instruction, multiple data) computing.The first GPU for neural networks was used by Kyoung-Su Oh, et al. for image processing published in 2004 (1). A minimum GPU parallel computer is composed of a CPU board and a GPU board. This book contains the important issue on which CPU/GPU board you should buy and also illustrates how to integrate them in a single box by considering the heat problem. The power consumption of GPU is so large that we should take care of the temperature and heat from the GPU board in the single box. Our goal is to have the faster parallel computer with lower power dissipation. Software installation is another critical issue for machine learning in Python. Two operating system examples including Ubuntu16.04 and Windows 10 system will be described. This book shows how to install CUDA and cudnnlib in two operating systems. Three frameworks including pytorch, keras, and chainer for machine learning on CUDA and cudnnlib will be introduced. Matching problems between operating system (Ubuntu, Windows 10), library (CUDA, cudnnlib), and machine learning framework (pytorch, keras, chainer) are discussed. The paper entitled "GPU" and "open source software" play a key role for advancing deep learning was published in Science (eLetter, July 20 2017)http://science.sciencemag.org/content/357/6346/16/tab-e-letters