High Performance Computing (Over 5,700 results)

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Paperback. Condition: 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.…

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Condition: Very Good. The computing power that's available on the average desktop has exploded in the past few years. A typical PC has performance exceeding that of a multi-million dollar supercomputer a mere decade ago. To some people, that might mean that it's time to sit back and watch computers get faster: performance is no longer an issue, we'll let hardware do the work. But if you're looking at this book, you're not one of them. Performance is always an issue. Even with the fastest computers, there's a need to harness the processing power and get more work done in a limited amount of time. If you're a software developer, you probably know that getting the most out of a modern workstation or PC can be tricky. Paying closer attention to memory reference patterns and loop structure can have a huge payoff. High Performance Computing discusses how modern workstations get their performance and how you can write code that makes optimal use of your hardware. You'll learn what the newest buzzwords really mean, how caching and other memory design features affect the way your software behaves, and where the newest "post-RISC" architectures are headed. If you're involved with purchasing or evaluating workstations, this book will help you make intelligent comparisons. You'll learn how to interpret the commonly quoted industry benchmarks, what vendors do to show their machines in the best possible light, and how to run your own benchmarks. Whether you're using the latest Pentium PC or a highly specialized multiprocessor, you'll find High Performance Computing an indispensable guide. Topics covered include: CPU and memory architecture for RISC microprocessors and their successors Optimizing compilers Timing and profiling programs Understanding parallelism Loop and memory reference optimizations Benchmarking Parallel Computing and Multiprocessing.…

paperback. Condition: Good.

paperback. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Hardcover. Condition: Very Good. 1. 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. Condition: 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.…