{"product_id":"gpu-operating-systems-and-parallel-thom-haagenrud-9798170519767","title":"GPU Operating Systems and Parallel Computing: A Practical Guide to CUDA, GPU Architecture, Parallel Programming, and High Performance Systems","description":"\u003cp\u003e\u003cb\u003eStop Treating the GPU Like a Faster CPU. Learn to Engineer It as a Complete Computing System.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eModern 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.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eGPU Operating Systems and Parallel Computing\u003c\/i\u003e gives you a practical, systems-level framework for understanding that entire stack.\u003c\/p\u003e\u003cp\u003eStarting 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.\u003c\/p\u003e\u003cp\u003eInside, you'll learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003eUnderstand the GPU software and hardware stack-and diagnose problems at the correct layer\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eThink in terms of warps, blocks, grids, streaming multiprocessors, memory hierarchies, and resource limits\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDesign parallel algorithms around independence, dependencies, locality, load balance, and scaling\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eBuild and troubleshoot a CUDA development environment\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eWrite correct CUDA kernels and reason about execution, synchronization, and launch configuration\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eOptimize memory access, data reuse, transfers, and shared-memory usage\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUse streams, events, CUDA Graphs, and concurrency to build efficient pipelines\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eProfile workloads systematically instead of relying on optimization folklore\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDebug memory errors, race conditions, synchronization failures, and numerical problems\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDesign multi-GPU workloads using topology awareness, collectives, MPS, and MIG\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eBuild reliable GPU applications with observability, testing, reproducibility, security, and deployment practices\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eEvaluate CUDA, HIP, SYCL, and abstraction layers when portability matters\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe book is designed for developers and engineers working in \u003cb\u003eGPU programming, HPC, machine learning, scientific computing, simulation, rendering, backend infrastructure, and high-performance systems\u003c\/b\u003e. It combines conceptual explanations with diagrams, tables, code examples, exercises, and practical engineering decision processes.\u003c\/p\u003e\u003cp\u003eMost 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.\u003c\/p\u003e\u003cp\u003eWhether you're learning CUDA, optimizing an existing GPU workload, building a multi-GPU service, or designing production accelerator infrastructure, this book helps you connect \u003cb\u003eparallel programming concepts to the physical realities of modern GPU systems\u003c\/b\u003e.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBuild faster GPU software-but more importantly, learn why it is fast, when it will fail, and how to make it dependable.\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Thom Haagenrud\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798170519767\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/29\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 176\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.49lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.44d","brand":"Thom Haagenrud","offers":[{"title":"Paperback","offer_id":50847653167359,"sku":"9798170519767","price":18.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/gpu-operating-systems-and-parallel-thom-haagenrud-9798170519767","provider":"WR Book House","version":"1.0","type":"link"}