Parallel Computing Optimization (107 results)

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

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Taschenbuch. Condition: 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. Condition: 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. Condition: Neu. Neuware - Have you ever wondered how modern software achieves extraordinary performance when processing massive datasets, running complex simulations, powering intelligent applications, or solving computational problems that would overwhelm traditional systems Have you wanted to move beyond basic programming and learn how professional developers build high-performance applications that fully utilize modern hardware This book was written for that journey.Whether you are a software developer, systems programmer, computer science student, researcher, engineer, or technology enthusiast, this guide provides a practical and structured path toward mastering modern high-performance software development using advanced programming techniques and accelerated computing principles.Performance is no longer optional. It is a competitive advantage.Modern applications must process more data, respond faster, scale efficiently, and make better use of available computing resources. This book explains not only how these systems work, but also why specific design decisions lead to better performance, reliability, and scalability.Inside, you will learn how to build a strong foundation in modern systems programming, understand concurrent and parallel execution models, optimize memory usage, improve computational efficiency, and design software that can take advantage of large-scale parallel processing environments.Learn the principles behind high-performance computing.Rather than focusing solely on theory, this book connects core concepts with practical implementation strategies. You will explore memory architecture, workload distribution, synchronization techniques, optimization methodologies, accelerated processing workflows, large-scale data handling, scientific computing applications, machine learning acceleration, simulation systems, and enterprise-grade software engineering practices.Turn complex concepts into practical skills.Every major topic is presented in a clear and progressive manner, allowing you to build knowledge step by step while developing an understanding of how modern computational systems are designed and optimized.By the end of this book, you will be able to think like a performance-focused software engineer, identify bottlenecks with confidence, design scalable solutions, and apply advanced programming techniques to demanding real-world workloads.Build faster. Scale smarter. Compute more efficiently.If your goal is to move beyond conventional application development and gain the knowledge needed to create high-performance software for data-intensive, compute-intensive, and future-ready systems, this book provides the knowledge, techniques, and practical insights to help you get there.The next level of software performance starts here.…

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Taschenbuch. Condition: Neu. Neuware - Have you ever wondered how modern AI systems process massive amounts of data in seconds Curious about how high-performance applications achieve extraordinary speed and efficiency Interested in harnessing the power of GPUs without sacrificing the safety and reliability of modern software development If those questions have crossed your mind, then Modern GPU Programming with Rust and CUDA 13 was written for you.What if you could combine the memory safety and performance of Rust with the immense computational power of CUDA-enabled GPUs What if you could develop applications capable of handling complex parallel workloads, accelerating AI systems, optimizing memory usage, and solving computational challenges at scale This book takes you beyond theory and into the practical world of modern GPU programming.Whether you're a software developer looking to expand your expertise, a systems programmer seeking greater performance, an AI engineer building accelerated applications, or a technology enthusiast eager to understand parallel computing, you'll discover the concepts, tools, and techniques needed to work confidently with Rust and CUDA.Have you struggled to understand parallel programming models Wondered how GPU architectures differ from traditional CPUs Asked yourself how professional developers optimize memory transfers, manage thousands of concurrent threads, and unlock maximum hardware performance You'll find clear explanations, practical guidance, and real-world insights designed to help you bridge the gap between understanding and implementation.As you progress through the book, you'll explore the foundations of GPU computing, learn how Rust enhances reliability and safety in high-performance environments, understand CUDA programming principles, master memory optimization strategies, and develop the skills required to build scalable, efficient, and responsive applications.You'll also gain valuable knowledge about performance tuning, concurrency management, GPU acceleration techniques, AI-oriented workloads, and modern development practices used in today's demanding computing environments.The future of computing is increasingly parallel, accelerated, and performance-driven. Are your skills ready for that future Whether your goal is to accelerate machine learning workflows, process large datasets, build high-performance software, or simply become a more capable developer, this book provides the knowledge and practical understanding needed to move forward with confidence.Don't just learn about GPU programming-master the skills that power the next generation of intelligent and high-performance applications.Get your copy today and start building faster, smarter, and more powerful software with Rust and CUDA 13.…

Taschenbuch. Condition: Neu. Neuware.

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Taschenbuch. Condition: 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. Condition: 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. Condition: 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. Condition: 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.…

Taschenbuch. Condition: Neu. Neuware - Are you looking to understand modern high-performance computing and get more from your C++ and GPU programming skills C++26 and CUDA 13.3 Performance Engineering offers a practical introduction to parallel computing, GPU programming, kernel development, memory optimization, and performance-focused application design. Whether you're exploring GPU acceleration for the first time or looking to improve existing programming techniques, this guide presents complex ideas in a clear and approachable way.How can you write more efficient code How do GPUs handle demanding workloads What techniques can help reduce unnecessary computation and improve application performance Explore these questions and more through a focused guide designed for programmers, students, developers, and computing enthusiasts interested in building a stronger understanding of performance engineering and modern parallel applications.…

Taschenbuch. Condition: Neu. Neuware - What if your Python programs could handle demanding computational tasks more efficiently Mastering GPU Programming with Python and CUDA 13.3 offers a practical introduction to GPU-based computing and the techniques used to build and optimize accelerated applications. Instead of focusing only on theory, this guide helps you understand the principles behind parallel execution, GPU kernels, memory usage, and performance optimization. How do GPU programs work Which tasks benefit from parallel execution How can memory access affect performance And what techniques can help you identify and improve computational bottlenecks You'll explore these questions through practical discussions of parallel programming, kernel design, memory management, data movement, performance analysis, and application optimization. The book is designed to help readers develop a clearer understanding of how computational workloads can be structured for accelerated hardware. Whether you are building your programming foundation or expanding your knowledge of high-performance computing, this guide provides a structured path toward understanding GPU-based application development and performance engineering.…

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