Nº de artículo:
271255154
GPU Parallel Computing: From Basics to Breakthroughs in GPU Programming (GPU Expert Engineering: Mastering Design Programming and Optimization)
Nº de artículo:
271255154
HNL 1432
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- Your Code Runs on the GPU. So Why Isn't It Fast?Here's the dirty secret of GPU programming: getting a kernel to run is easy. Getting it to actually use the hardware — that's where careers are made. And it's exactly what most CUDA books never teach you.They hand you syntax. API listings. Toy examples that fall over the moment you profile them. Then you're on your own with a $30,000 GPU running at 4% of its capability — and no idea why.This book teaches the thing that separates GPU programmers who get hired from programmers who once tried CUDA: judgment.When to use a library and when to write the kernel. When to fuse, when to tile, when to stop optimizing — and when the CPU was the right answer all along. The engineering discipline of proving a speedup instead of guessing at one.Inside you'll discover:The first question that predicts whether a workload belongs on a GPU at all — before you waste a week porting code that was never going to winWhy correct comes before fast — a complete validation and profiling workflow (compute-sanitizer, Nsight Systems, Nsight Compute) that turns performance claims into evidenceThe memory hierarchy playbook — coalescing, shared-memory tiling, register pressure, and the one performance rule that governs everything elseThe parallel primitives every real program is built from — reductions, scans, histograms, sorting — with complete, validated, production-shaped code, not fragmentsTensor Cores and low-precision compute demystified — FP16, BF16, FP8, and why the AI industry runs on matrix hardware most programmers never touchThe modern stack the classic textbooks skip — Triton, CuPy, Numba, torch.compile, CUTLASS, and the libraries-first strategy professionals actually useMulti-GPU and inference at scale — NVLink, NCCL, KV-cache, quantized serving: how single-kernel skills become system skillsFive end-to-end case studies — from naive baseline to optimized implementation, with the measurements, the mistakes, and the stopping rulesEvery substantial example is a complete program with error handling and numerical validation, backed by a companion code repository. No pseudocode. No left as an exercise.Who this is for: working C++ and Python programmers, ML engineers who want to understand what's under their framework, and scientific computing practitioners ready to think in throughput. You should already know how to program. You should not already need to know CUDA.Who it's not for: anyone wanting a gentle first-programming book. This assumes competence and rewards it.GPU skills are commanding some of the highest salaries in software — because so few programmers can reason about the hardware instead of merely invoking it. This book is the bridge from basics to breakthroughs.Scroll up and click Buy Now — and start reading a GPU workload like an engineer instead of a tourist.
| Publisher | Independently published |
| Publication date | 5 July 2026 |
| Language | English |
| Print length | 411 pages |
| ISBN-13 | 979-8185689622 |
| Dimensions | 21.59 x 2.36 x 27.94 cm |
| Part of series | GPU Expert Engineering: Mastering Design, Programming, and Optimization |
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English edition Gareth Thomas Format: Paperback Editorial Review
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