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  • 1
    Online Resource
    Online Resource
    CUDA application design and development / (2012)
    Waltham, MA :Morgan Kaufmann,
    Details
    UID:
    almahu_9949697560002882
    Format: 1 online resource (337 p.)
    Edition: 1st edition
    ISBN: 1-283-29903-8 , 9786613299031 , 0-12-388432-2
    Series Statement: Applications of GPU computing series CUDA application design and development
    Content: As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA Application Design and Development starts with an introduction to parallel computing concepts for readers with no previous parallel experience, and focuses on issues of immediate importance to working software developers: achieving high performance, maintaining competitiveness, analyzing CUDA benefits versus costs, and det
    Note: Description based upon print version of record. , Front Cover; CUDA Application Design and Development; Copyright; Dedication; Table of Contents; Foreword; Preface; 1 First Programs and How to Think in CUDA; Source Code and Wiki; Distinguishing CUDA from Conventional Programming with a Simple Example; Choosing a CUDA API; Some Basic CUDA Concepts; Understanding Our First Runtime Kernel; Three Rules of GPGPU Programming; Rule 1: Get the Data on the GPU and Keep It There; Rule 2: Give the GPGPU Enough Work to Do; Rule 3: Focus on Data Reuse within the GPGPU to Avoid Memory Bandwidth Limitations; Big-O Considerations and Data Transfers , CUDA and Amdahl's LawData and Task Parallelism; Hybrid Execution: Using Both CPU and GPU Resources; Regression Testing and Accuracy; Silent Errors; Introduction to Debugging; UNIX Debugging; NVIDIA's cuda-gdb Debugger; The CUDA Memory Checker; Use cuda-gdb with the UNIX ddd Interface; Windows Debugging with Parallel Nsight; Summary; 2 CUDA for Machine Learning and Optimization; Modeling and Simulation; Fitting Parameterized Models; Nelder-Mead Method; Levenberg-Marquardt Method; Algorithmic Speedups; Machine Learning and Neural Networks; XOR: An Important Nonlinear Machine-Learning Problem , An Example Objective FunctionA Complete Functor for Multiple GPU Devices and the Host Processors; Brief Discussion of a Complete Nelder-Mead Optimization Code; Performance Results on XOR; Performance Discussion; Summary; The C++ Nelder-Mead Template; 3 The CUDA Tool Suite: Profiling a PCA/NLPCA Functor; PCA and NLPCA; Autoencoders; An Example Functor for PCA Analysis; An Example Functor for NLPCA Analysis; Obtaining Basic Profile Information; Gprof: A Common UNIX Profiler; The NVIDIA Visual Profiler: Computeprof; Parallel Nsight for Microsoft Visual Studio; The Nsight Timeline Analysis , The NVTX Tracing LibraryScaling Behavior of the CUDA API; Tuning and Analysis Utilities (TAU); Summary; 4 The CUDA Execution Model; GPU Architecture Overview; Thread Scheduling: Orchestrating Performance and Parallelism via the Execution Configuration; Relevant computeprof Values for a Warp; Warp Divergence; Guidelines for Warp Divergence; Relevant computeprof Values for Warp Divergence; Warp Scheduling and TLP; Relevant computeprof Values for Occupancy; ILP: Higher Performance at Lower Occupancy; ILP Hides Arithmetic Latency; ILP Hides Data Latency; ILP in the Future , Relevant computeprof Values for Instruction RatesLittle's Law; CUDA Tools to Identify Limiting Factors; The nvcc Compiler; Launch Bounds; The Disassembler; PTX Kernels; GPU Emulators; Summary; 5 CUDA Memory; The CUDA Memory Hierarchy; GPU Memory; L2 Cache; Relevant computeprof Values for the L2 Cache; L1 Cache; Relevant computeprof Values for the L1 Cache; CUDA Memory Types; Registers; Local memory; Relevant computeprof Values for Local Memory Cache; Shared Memory; Relevant computeprof Values for Shared Memory; Constant Memory; Texture Memory; Relevant computeprof Values for Texture Memory , Global Memory , English
    Additional Edition: ISBN 0-12-388426-8
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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    HU Berlin
    FU Berlin
    Charité
  • 2
    Online Resource
    Online Resource
    CUDA application design and development (2012)
    Waltham, MA : Morgan Kaufmann
    Details
    UID:
    gbv_1651320322
    Format: Online Ressource
    Edition: Online-Ausg.
    ISBN: 0123884268 , 9780123884329 , 0123884322 , 9780123884268 , 9781283299039
    Series Statement: Applications of GPU computing series
    Content: As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA Application Design and Development starts with an introduction to parallel computing concepts for readers with no previous parallel experience, and focuses on issues of immediate importance to working software developers: achieving high performance, maintaining competitiveness, analyzing CUDA benefits versus costs, and determining application lifespan. The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries Using an approach refined in a series of well-received articles at Dr Dobb's Journal, author Rob Farber takes the reader step-by-step from fundamentals to implementation, moving from language theory to practical coding Includes multiple examples building from simple to more complex applications in four key areas: machine learning, visualization, vision recognition, and mobile computing Addresses the foundational issues for CUDA development: multi-threaded programming and the different memory hierarchy Includes teaching chapters designed to give a full understanding of CUDA tools, techniques and structure. Presents CUDA techniques in the context of the hardware they are implemented on as well as other styles of programming that will help readers bridge into the new material
    Note: Machine generated contents note: 1. How to think in CUDA 2. Tools to build, debug and profile 3. The GPU performance envelope 4. The CUDA memory subsystems 5. Exploiting the CUDA execution grid 6. MultiGPU applications and scaling 7. Numerical CUDA, libraries and high-level language bindings 8. Mixing CUDA with rendering 9. High Performance Machine Learning 10. Scientific Visualization 11. Multimedia with OpenCV 12. Ultra Low-power Devices: Tegra. - Description based on print version record , 1. How to think in CUDA 2. Tools to build, debug and profile 3. The GPU performance envelope 4. The CUDA memory subsystems 5. Exploiting the CUDA execution grid 6. MultiGPU applications and scaling 7. Numerical CUDA, libraries and high-level language bindings 8. Mixing CUDA with rendering 9. High Performance Machine Learning 10. Scientific Visualization 11. Multimedia with OpenCV 12. Ultra Low-power Devices: Tegra. , As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA Application Design and Development starts with an introduction to parallel computing concepts for readers with no previous parallel experience, and focuses on issues of immediate importance to working software developers: achieving high performance, maintaining competitiveness, analyzing CUDA benefits versus costs, and determining application lifespan. The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries Using an approach refined in a series of well-received articles at Dr Dobb's Journal, author Rob Farber takes the reader step-by-step from fundamentals to implementation, moving from language theory to practical coding Includes multiple examples building from simple to more complex applications in four key areas: machine learning, visualization, vision recognition, and mobile computing Addresses the foundational issues for CUDA development: multi-threaded programming and the different memory hierarchy Includes teaching chapters designed to give a full understanding of CUDA tools, techniques and structure. Presents CUDA techniques in the context of the hardware they are implemented on as well as other styles of programming that will help readers bridge into the new material , 1. How to think in CUDA 2. Tools to build, debug and profile 3. The GPU performance envelope 4. The CUDA memory subsystems 5. Exploiting the CUDA execution grid 6. MultiGPU applications and scaling 7. Numerical CUDA, libraries and high-level language bindings 8. Mixing CUDA with rendering 9. High Performance Machine Learning 10. Scientific Visualization 11. Multimedia with OpenCV 12. Ultra Low-power Devices: Tegra
    Additional Edition: ISBN 1283299038
    Additional Edition: ISBN 9781283299039
    Additional Edition: ISBN 9786613299031
    Additional Edition: ISBN 6613299030
    Additional Edition: ISBN 9780123884268
    Additional Edition: Druckausg. Farber, Rob CUDA application design and development Amsterdam : Elsevier Morgan Kaufman, 2011 ISBN 0123884268
    Additional Edition: ISBN 9780123884268
    Language: English
    Subjects: Computer Science
    RVK:
    ST 150
    RVK:
    ST 230
    RVK:
    ST 320
    Keywords: Parallelverarbeitung ; Programmierung ; Grafikprozessor ; CUDA ; CUDA ; Grafikprozessor ; Programmierung ; Electronic books ; Electronic books ; Electronic books
    URL: lizenzpflichtig
    URL: Volltext
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Online Access
    UB Potsdam
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