ISBN: 9798170519767 - gpu operating systems and parallel computing: a practical guide to cuda, gpu architecture, parallel programming, and high performance systems (6 resultados)

Taschenbuch. Condición: 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.…

Paperback. Condición: new. Paperback. 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 layerThink in terms of warps, blocks, grids, streaming multiprocessors, memory hierarchies, and resource limitsDesign parallel algorithms around independence, dependencies, locality, load balance, and scalingBuild and troubleshoot a CUDA development environmentWrite correct CUDA kernels and reason about execution, synchronization, and launch configurationOptimize memory access, data reuse, transfers, and shared-memory usageUse streams, events, CUDA Graphs, and concurrency to build efficient pipelinesProfile workloads systematically instead of relying on optimization folkloreDebug memory errors, race conditions, synchronization failures, and numerical problemsDesign multi-GPU workloads using topology awareness, collectives, MPS, and MIGBuild reliable GPU applications with observability, testing, reproducibility, security, and deployment practicesEvaluate 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. 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 layerThink in terms of warps, blocks, grids, streaming multiprocessors, memory hierarchies, and resource limitsDesign parallel algorithms around independence, dependencies, locality, load balance, and scalingBuild and troubleshoot a CUDA development environmentWrite correct CUDA kernels and reason about execution, synchronization, and launch configurationOptimize memory access, data reuse, transfers, and shared-memory usageUse streams, events, CUDA Graphs, and concurrency to build efficient pipelinesProfile workloads systematically instead of relying on optimization folkloreDebug memory errors, race conditions, synchronization failures, and numerical problemsDesign multi-GPU workloads using topology awareness, collectives, MPS, and MIGBuild reliable GPU applications with observability, testing, reproducibility, security, and deployment practicesEvaluate 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…