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BL
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872341
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| Title & Author
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Advances in GPU research and practice /\ edited by Hamid Sarbazi-Azad.
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| Publication Statement
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Amsterdam :: Elsevier,, [2017]
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, ©2017
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| Series Statement
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Emerging Trends in Computer Science and Applied Computing
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| Page. NO
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1 online resource (776 pages) :: illustrations (some color)
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| ISBN
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0128037385
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: 0128037881
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: 9780128037386
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: 9780128037881
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0128037385
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| Bibliographies/Indexes
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ReferencesChapter 2: SnuCL: A unified OpenCL framework for heterogeneous clusters; 1 Introduction; 2 OpenCL; 2.1 Platform Model; 2.2 Execution Model; 2.3 Memory Model; 2.4 Synchronization; 2.5 Memory Consistency; 2.6 OpenCL ICD; 3 Overview of SnuCL framework; 3.1 Limitations of OpenCL; 3.2 SnuCL CPU; 3.3 SnuCL Single; 3.4 SnuCL Cluster; 3.4.1 Processing synchronization commands; 4 Memory management in SnuCL Cluster; 4.1 Space Allocation to Memory Objects; 4.2 Minimizing Copying Overhead; 4.3 Processing Memory Commands; 4.4 Consistency Management.
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| Contents
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Front Cover; Advances in GPU Research and Practice; Copyright; Dedication; Contents; List of Contributors; Preface; Acknowledgments; Part 1: Programming and tools; Chapter 1: Formal analysis techniques for reliable GPU programming: current solutions and call to action; 1 GPUs in Support of Parallel Computing; Bugs in parallel and GPU code; 2 A quick introduction to GPUs; Organization of threads; Memory spaces; Barrier synchronization; Warps and lock-step execution; Dot product example; 3 Correctness issues in GPU programming; Data races; Lack of forward progress guarantees.
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3 Built-In Atomic Functions on Regular Variables4 Fine-Grained Communication and Synchronization; 4.1 Memory Consistency Model; 4.1.1 Sequential consistency; 4.1.2 Relaxed consistency; 4.2 The OpenCL 2.0 Memory Model; 4.2.1 Relationships between two memory operations; 4.2.2 Special atomic operations and stand-alone memory fence; 4.2.3 Release and acquire semantics; 4.2.4 Memory order parameters; 4.2.5 Memory scope parameters; 5 Conclusion and Future Research Direction; References; Chapter 4: Software-level task scheduling on GPUs; 1 Introduction, Problem Statement, and Context.
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4.5 Detecting Memory Objects Written by a Kernel5 SnuCL extensions to OpenCL; 6 Performance evaluation; 6.1 Evaluation Methodology; 6.2 Performance; 6.2.1 Scalability on the medium-scale GPU cluster; 6.2.2 Scalability on the large-scale CPU cluster; 7 Conclusions; Acknowledgments; References; Chapter 3: Thread communication and synchronization on massively parallel GPUs; 1 Introduction; 2 Coarse-Grained Communication and Synchronization; 2.1 Global Barrier at the Kernel Level; 2.2 Local Barrier at the Work-Group Level; 2.3 Implicit Barrier at the Wavefront Level.
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Floating-point accuracy4 The need for effective tools; 4.1 A Taxonomy of Current Tools; 4.2 Canonical Schedules and the Two-Thread Reduction; Race freedom implies determinism; Detecting races: ``all for one and one for all''; Restricting to a canonical schedule; Reduction to a pair of threads; 4.3 Symbolic Bug-Finding Case Study: GKLEE; 4.4 Verification Case Study: GPUVerify; 5 Call to Action; GPUs will become more pervasive; Current tools show promise; Solving basic correctness issues; Equivalence checking; Clarity from vendors and standards bodies; User validation of tools; Acknowledgments.
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| Abstract
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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.
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| Subject
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Computer graphics.
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Graphics processing units-- Programming.
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Image processing-- Digital techniques.
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Imaging systems.
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Computer graphics.
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COMPUTERS-- General.
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Image processing-- Digital techniques.
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Imaging systems.
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| Dewey Classification
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006.6869
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| LC Classification
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T385
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| Added Entry
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Sarbazi-Azad, Hamid
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