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“This book is required reading for anyone working with accelerator-based computing systems.” –From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.   CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.   Major topics covered include Parallel programming Thread cooperation Constant memory and events Texture memory Graphics interoperability Atomics Streams CUDA C on multiple GPUs Advanced atomics Additional CUDA resources All the CUDA software tools you’ll need are freely available for download from NVIDIA.http://developer.nvidia.com/object/cuda-by-example.html

“This book is required reading for anyone working with accelerator-based computing systems.” –From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.   CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.   Major topics covered include Parallel programming Thread cooperation Constant memory and events Texture memory Graphics interoperability Atomics Streams CUDA C on multiple GPUs Advanced atomics Additional CUDA resources All the CUDA software tools you’ll need are freely available for download from NVIDIA.http://developer.nvidia.com/object/cuda-by-example.html

GPU Architecture: NVIDIA Ada Lovelace NVIDIA CUDA Parallel Processing Cores: 18,176 NVIDIA Tensor Cores (4th gen): 568 NVIDIA RT Cores (3rd Gen): 142 Peak FP32 performance (non-Tensor): 90.5 TFLOPS Peak FP16 Tensor performance with FP16 Accumulate: 181.05 TFLOPS, 362.1 TFLOPS* Peak Tensor Float 32 (TF32) performance: 90.5 TFLOPS, 181 TFLOPS* Peak Bfloat16 (BF16) performance with FP32 Accumulate: 181.05 TFLOPS, 362.1 TFLOPS* Peak FP8 Tensor performance: 362 TFLOPS, 724 TFLOPS* Peak Integer Performance: INT8: 362 TOPS, 724 TOPS*, INT4: 724 TOPS, 1448 TOPS* RT Core performance: 209 TFLOPS GPU Memory: 48 GB GDDR6 Memory Bandwidth: 864 GB/s ECC: Yes NVIDIA NVLink: No support System Interface: PCIe Gen 4, x16 lanes Form Factor: PCIe full height/length, double width (10.5" x 4.4") Multi-Instance GPU (MIG): No support Max Power Consumption: 300 W Thermal Solution: Passive vGPU Software Support: NVIDIA vPC/vApps, NVIDIA RTX Virtual Workstation (vWS) Display connectors: 4x DisplayPort 1.4a (disabled by default**) Max Simultaneous Displays: Up to four 5K Monitors at 60Hz per card or dual 8K displays @ 60Hz (requires displayPort 1.4 DSC); Each display port can support 4K at 120 Hz with 30-bit color Graphics APIs: DirectX 12 Ultimate, Shader Model 6.6, OpenGL 4.6, Vulkan 1.3 Compute APIs: CUDA 12.0, Direct Compute, OpenCL 3.0 View specifications on Lenovo Press

Programming Massively Parallel Processors discusses the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This book describes computational thinking techniques that will enable students to think about problems in ways that are amenable to high-performance parallel computing. It utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments. Studies learn how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL. This book is recommended for advanced students, software engineers, programmers, and hardware engineers.Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing.Utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments.Shows you how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL.

Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth. For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computingUtilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environmentsContains new and updated case studiesIncludes coverage of newer libraries, such as CuDNN for Deep Learning

Programming Massively Parallel Processors discusses the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This book describes computational thinking techniques that will enable students to think about problems in ways that are amenable to high-performance parallel computing. It utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments. Studies learn how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL. This book is recommended for advanced students, software engineers, programmers, and hardware engineers.Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing.Utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments.Shows you how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL.

Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth. For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computingUtilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environmentsContains new and updated case studiesIncludes coverage of newer libraries, such as CuDNN for Deep Learning