Parallel computing optimization (115 résultats)

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Paperback or Softback. Etat : New. Input/Output Intensive Massively Parallel Computing: Language Support, Automatic Parallelization, Advanced Optimization, and Runtime Systems. Book.

Taschenbuch. Etat : Neu. Neuware - Modern applications in artificial intelligence, scientific computing, computer vision, financial modeling, game development, and data analytics increasingly rely on GPU acceleration to process massive workloads efficiently. GPU Programming with Modern C++ and CUDA provides a practical, project-driven approach to mastering GPU programming, enabling you to build scalable, high-performance applications using NVIDIA's CUDA platform and modern C++.Designed for developers who want to move beyond traditional CPU programming, this book introduces the principles of parallel computing before guiding you through CUDA fundamentals, GPU architecture, memory management, kernel programming, synchronization, optimization strategies, and multi-GPU development. Every concept is reinforced through real-world examples and hands-on projects that help you understand not only how CUDA works but also why specific design choices lead to better performance.As your knowledge grows, you'll explore advanced topics including asynchronous execution with CUDA streams, shared memory optimization, occupancy tuning, performance profiling, debugging GPU applications, and integrating GPU-accelerated code into larger software systems. You'll also learn how to build reusable GPU libraries that can be accessed from Python and other programming environments, making your solutions portable across modern development ecosystems.Whether you're accelerating machine learning pipelines, processing large scientific datasets, or building compute-intensive applications, this book equips you with the practical techniques and performance engineering skills needed to develop efficient GPU software with confidence.What You'll Learn- Understand the foundations of parallel computing and GPU architecture.- Set up a complete CUDA development environment using modern C++.- Write efficient CUDA kernels for high-performance parallel execution.- Manage GPU memory effectively for maximum throughput.- Optimize applications using shared memory, streams, and asynchronous execution.- Improve performance through profiling, benchmarking, and optimization techniques.- Scale applications across multiple GPUs.- Build reusable GPU libraries and integrate them with Python applications.- Apply GPU programming techniques to AI, scientific computing, image processing, simulation, and data-intensive workloads.- Adopt industry best practices for writing maintainable, scalable, and production-ready CUDA applications.Who This Book Is ForThis book is intended for C++ developers, software engineers, AI and machine learning practitioners, game developers, data scientists, researchers, and high-performance computing professionals who want to leverage GPU acceleration to solve computationally intensive problems. A working knowledge of modern C++ is recommended, while prior CUDA experience is helpful but not required.…

Taschenbuch. Etat : Neu. Neuware - A practical guide to high-performance CUDA development for engineers, researchers, and developers who need more than introductory examples. This book focuses on the full workflow of GPU computing, from understanding how streaming multiprocessors execute warps to building maintainable, testable, and scalable applications for real scientific workloads.The chapters move from core architecture and programming fundamentals into profiling, memory tuning, numerical accuracy, and multi-GPU scaling. You will see how to turn a correct kernel into an efficient one, how to measure bottlenecks with Nsight tools, and how to make informed tradeoffs between occupancy, bandwidth, latency, and precision.What this book covers- GPU architecture and execution behavior, including warps, scheduling, memory hierarchy, and data movement costs.- CUDA kernel design, with launch configuration, indexing, synchronization, debugging, and reusable interfaces.- Performance engineering, using profiling metrics and iterative optimization based on measured results.- Memory optimization, including coalescing, shared memory tiling, register pressure, cache behavior, and data layout.- Common scientific patterns, such as stencils, reductions, scans, sparse formats, and batched linear algebra.- Numerical correctness, with floating point behavior, stable summation, boundary handling, and CPU validation.- Advanced coordination techniques, such as warp and block level operations, streams, events, and asynchronous overlap.- Host and multi-GPU engineering, covering pinned memory, unified memory, partitioning strategies, NCCL, halo exchange, and scaling studies.Why it stands out- Engineering-first approach, centered on real optimization decisions rather than isolated syntax.- Workflow oriented, with profiling, testing, benchmarking, and regression tracking built into the discussion.- Useful for scientific computing, especially stencil solvers, sparse methods, reductions, and iterative pipelines.- Built for maintainability, with guidance on project structure, code reuse, and repeatable validation.Ideal for anyone who wants to write CUDA code that is not only correct, but also fast, traceable, and ready for production-scale workloads.…

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Taschenbuch. Etat : Neu. Neuware.

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Massively parallel processing is currently the most promising answer to the quest for increased computer performance. This has resulted in the development of new programming languages and programming environments and has stimulated the design and production of massively parallel supercomputers. The efficiency of concurrent computation and input/output essentially depends on the proper utilization of specific architectural features of the underlying hardware. This book focuses on development of runtime systems supporting execution of parallel code and on supercompilers automatically parallelizing code written in a sequential language. Fortran has been chosen for the presentation of the material because of its dominant role in high-performance programming for scientific and engineering applications.…

Taschenbuch. Etat : Neu. Bees Swarm Optimization for Association Rule Mining on CUDA | Enactment of Bio Inspired Bees Swarm Intelligence for Association Rule Mining using Parallel Computing Platform | Alankar Shelar | Taschenbuch | 64 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139836338 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

Taschenbuch. Etat : Neu. Towards Parallel Computing by Patterns, Algorithms and Optimization | Mohammad Sultan Mahmud (u. a.) | Taschenbuch | 52 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330028197 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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Taschenbuch. Etat : Neu. Advanced optimization techniques for MT simulation on GPUs | Using massively parallel devices for scientific computing | Eyad Hailat | Taschenbuch | 132 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659554636 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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