High performance parallel computing (110 Ergebnisse)

Paperback. Zustand: Good. CUDA for Engineers gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do computations on a gaming-level PC that would have required a supercomputer just a few years ago.   The authors introduce the essentials of CUDA C programming clearly and concisely, quickly guiding you from running sample programs to building your own code. Throughout, you?ll learn from complete examples you can build, run, and modify, complemented by additional projects that deepen your understanding. All projects are fully developed, with detailed building instructions for all major platforms.   Ideal for any scientist, engineer, or student with at least introductory programming experience, this guide assumes no specialized background in GPU-based or parallel computing. In an appendix, the authors also present a refresher on C programming for those who need it.   Coverage includes Preparing your computer to run CUDA programsUnderstanding CUDA?s parallelism model and C extensionsTransferring data between CPU and GPUManaging timing, profiling, error handling, and debuggingCreating 2D gridsInteroperating with OpenGL to provide real-time user interactivityPerforming basic simulations with differential equationsUsing stencils to manage related computations across threadsExploiting CUDA?s shared memory capability to enhance performanceInteracting with 3D data: slicing, volume rendering, and ray castingUsing CUDA librariesFinding more CUDA resources and code   Realistic example applications include Visualizing functions in 2D and 3DSolving differential equations while changing initial or boundary conditionsViewing/processing images or image stacksComputing inner products and centroidsSolving systems of linear algebraic equationsMonte-Carlo computations.…

Paperback. Zustand: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Zustand: Very Good. GPUs are designed for maximum throughput, but are subject to low-level subtleties. In contrast, Python is a high-level language that favours ease of use over speed. In this book, we will combine the power of both Python and CUDA to help you create high performing Python applications by using open-source libraries such as PyCUDA and SciKit-CUDA.…

Paperback. Zustand: Good. GPUs are designed for maximum throughput, but are subject to low-level subtleties. In contrast, Python is a high-level language that favours ease of use over speed. In this book, we will combine the power of both Python and CUDA to help you create high performing Python applications by using open-source libraries such as PyCUDA and SciKit-CUDA.…

Hardcover. Zustand: Sehr gut. 444 S.; Ill. Very good. Shrink wrapped. Sprache: Englisch Gewicht in Gramm: 1100.

Zustand: New. In English.

Softcover. Zustand: Très bon. Ancien livre de bibliothèque avec équipements. Edition 2016. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2016. Ammareal gives back up to 15% of this item's net price to charity organizations.…

2015. 16 x 24 cm. X, 229 S. X, 229 p. 127 illus., 109 illus. in color. Hardcover. Sprache: Englisch.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. pp. 352.

Hardcover. Zustand: Wie neu. 527 S.; Ill. Like new. Shrink wrapped. Sprache: Englisch Gewicht in Gramm: 1295.

Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: New. In English.

Taschenbuch. Zustand: 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.…

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - Your CPU is the bottleneck. Your GPU capable of thousands of parallel operations is sitting there, mostly idle.Modern AI training, scientific simulation, and high-performance computing all run on the same insight: some problems are far better solved by thousands of simple operations running in parallel than by a handful of powerful sequential ones. CUDA C++ is how you tap into that power directly, and this book takes you from writing your very first kernel to genuinely optimized, production-grade GPU code.Inside, you'll learn to: Understand GPU architecture at a level that actually changes how you write codeWrite, launch, and debug CUDA kernels with real confidence, not trial and errorMaster the thread, block, and grid hierarchy that underlies all CUDA programmingNavigate global, shared, and constant memory and know which to use whenOptimize for real, measurable performance gains, not just 'it runs on the GPU now'Apply CUDA C++ to AI training workloads, scientific computing, and large-scale data processingUse profiling tools to find and fix bottlenecks in your own kernelsScale from single-GPU experiments to more advanced parallel computing patternsThis book is written for C++ developers who are ready to move into high-performance and AI-adjacent engineering roles the kind of roles where understanding what's happening at the hardware level is what separates 'it works' from 'it's fast.' Rather than treating GPU programming as an intimidating black box, this guide builds your understanding progressively, with a structured path from fundamentals through advanced optimization techniques used in real machine learning and HPC systems.The AI infrastructure boom model training, inference optimization, large-scale simulation runs on GPU code written by people who understand CUDA. This book is how you become one of them.Get your copy today and put your GPU to work instead of letting it idle.…

Taschenbuch. Zustand: Neu. Neuware - Stop Treating the GPU Like a Faster CPU. Learn to Engineer It as a Complete Computing System.Modern GPU development is no longer just about writing a CUDA kernel and hoping it runs faster. Real performance depends on the entire path from application code to driver, memory system, scheduler, hardware, and production infrastructure.GPU Operating Systems and Parallel Computing gives you a practical, systems-level framework for understanding that entire stack.Starting with the GPU operating environment, this guide explains how host operating systems, drivers, runtimes, contexts, queues, memory mappings, firmware, and hardware schedulers work together. From there, it moves into GPU architecture, parallel decomposition, CUDA programming, memory optimization, asynchronous execution, profiling, debugging, multi-GPU computing, and production deployment.Inside, you'll learn how to: - Understand the GPU software and hardware stack-and diagnose problems at the correct layer- Think in terms of warps, blocks, grids, streaming multiprocessors, memory hierarchies, and resource limits- Design parallel algorithms around independence, dependencies, locality, load balance, and scaling- Build and troubleshoot a CUDA development environment- Write correct CUDA kernels and reason about execution, synchronization, and launch configuration- Optimize memory access, data reuse, transfers, and shared-memory usage- Use streams, events, CUDA Graphs, and concurrency to build efficient pipelines- Profile workloads systematically instead of relying on optimization folklore- Debug memory errors, race conditions, synchronization failures, and numerical problems- Design multi-GPU workloads using topology awareness, collectives, MPS, and MIG- Build reliable GPU applications with observability, testing, reproducibility, security, and deployment practices- Evaluate CUDA, HIP, SYCL, and abstraction layers when portability mattersThe book is designed for developers and engineers working in GPU programming, HPC, machine learning, scientific computing, simulation, rendering, backend infrastructure, and high-performance systems. It combines conceptual explanations with diagrams, tables, code examples, exercises, and practical engineering decision processes.Most importantly, it teaches a repeatable approach to GPU performance: establish correctness, measure the complete workload, identify the real bottleneck, make a targeted change, and validate the result.Whether you're learning CUDA, optimizing an existing GPU workload, building a multi-GPU service, or designing production accelerator infrastructure, this book helps you connect parallel programming concepts to the physical realities of modern GPU systems.Build faster GPU software-but more importantly, learn why it is fast, when it will fail, and how to make it dependable.…

Taschenbuch. Zustand: Neu. Neuware.

Zustand: New. pp. 330.

Paperback. Zustand: Brand New. 1st edition. 352 pages. 9.50x7.25x1.00 inches. In Stock.

Taschenbuch. Zustand: 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.…

Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - CUDA for Engineers gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do computations on a gaming-level PC that would have required a supercomputer just a few years ago. The authors introduce the essentials of CUDA C programming clearly and concisely, quickly guiding you from running sample programs to building your own code. Throughout, you ll learn from complete examples you can build, run, and modify, complemented by additional projects that deepen your understanding. All projects are fully developed, with detailed building instructions for all major platforms. Ideal for any scientist, engineer, or student with at least introductory programming experience, this guide assumes no specialized background in GPU-based or parallel computing. In an appendix, the authors also present a refresher on C programming for those who need it. Coverage includes Preparing your computer to run CUDA programsUnderstanding CUDA s parallelism model and C extensionsTransferring data between CPU and GPUManaging timing, profiling, error handling, and debuggingCreating 2D gridsInteroperating with OpenGL to provide real-time user interactivityPerforming basic simulations with differential equationsUsing stencils to manage related computations across threadsExploiting CUDA s shared memory capability to enhance performanceInteracting with 3D data: slicing, volume rendering, and ray castingUsing CUDA librariesFinding more CUDA resources and code Realistic example applications include Visualizing functions in 2D and 3DSolving differential equations while changing initial or boundary conditionsViewing/processing images or image stacksComputing inner products and centroidsSolving systems of linear algebraic equationsMonte-Carlo computations.…

Taschenbuch. Zustand: 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.…

Zustand: New. In English.