CUDA Programming in 21 Days: A Hands-On Course C++ and Python

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Bol CUDA Programming in 21 Days A Hands-On Course in C++ and Python >================================================================ Go from "what even is a GPU?" to writing, debugging, and tuning your own CUDA kernels - in three focused weeks. >Most GPU books are either a wall of reference material or a thin layer of copy-paste recipes. This one is a course. Each day is a single sitting that builds on the last, teaches one idea properly, and ends with a workshop so the knowledge lands in your >You write REAL CUDA C++ that compiles with nvcc, and you see the Python equivalent (CuPy and Numba) alongside every step, so you can run and experiment even before your C++ is fluent. Every >WHAT YOU GET - 21 chapters (3 weeks x 7 days), 1.2 million words of careful, worked teaching - no filler. - 229 figures, diagrams, and plots, every one generated from a real computation or a clean schematic. - Hundreds of runnable listings in CUDA C++, CuPy, and Numba. >THE THREE WEEKS Week 1 - Get onto the GPU: why GPUs win, the toolkit, your first kernel, thread indexing, moving data, and debugging. Week 2 - Make it fast: the memory hierarchy, coalescing, shared memory and tiling, synchronization, warps and divergence, occupancy, and honest profiling with a roofline. Week 3 - Patterns, libraries, and a real project: reduction, atomics, scan, streams and overlap, Thrust/cuBLAS/cuFFT/CuPy, >WHO IT'S FOR You know a little C or C++ and a little Python. You do NOT need any GPU experience. You do not even need an expensive GPU - any recent NVIDIA card works, and Day 2 shows you how to run every >BY DAY 21 YOU CAN - decide whether a problem suits a GPU, and why; - write, launch, and debug your own kernels; - lay out memory and choose a launch configuration for speed; - use reductions, scans, atomics, and streams with confidence; - reach for the right library - and verify its result; >The GPU stops being a black box. Go build something that needed all those threads.

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CUDA Programming in 21 Days A Hands-On Course in C++ and Python >================================================================ Go from "what even is a GPU?" to writing, debugging, and tuning your own CUDA kernels - in three focused weeks. >Most GPU books are either a wall of reference material or a thin layer of copy-paste recipes. This one is a course. Each day is a single sitting that builds on the last, teaches one idea properly, and ends with a workshop so the knowledge lands in your >You write REAL CUDA C++ that compiles with nvcc, and you see the Python equivalent (CuPy and Numba) alongside every step, so you can run and experiment even before your C++ is fluent. Every >WHAT YOU GET - 21 chapters (3 weeks x 7 days), 1.2 million words of careful, worked teaching - no filler. - 229 figures, diagrams, and plots, every one generated from a real computation or a clean schematic. - Hundreds of runnable listings in CUDA C++, CuPy, and Numba. >THE THREE WEEKS Week 1 - Get onto the GPU: why GPUs win, the toolkit, your first kernel, thread indexing, moving data, and debugging. Week 2 - Make it fast: the memory hierarchy, coalescing, shared memory and tiling, synchronization, warps and divergence, occupancy, and honest profiling with a roofline. Week 3 - Patterns, libraries, and a real project: reduction, atomics, scan, streams and overlap, Thrust/cuBLAS/cuFFT/CuPy, >WHO IT'S FOR You know a little C or C++ and a little Python. You do NOT need any GPU experience. You do not even need an expensive GPU - any recent NVIDIA card works, and Day 2 shows you how to run every >BY DAY 21 YOU CAN - decide whether a problem suits a GPU, and why; - write, launch, and debug your own kernels; - lay out memory and choose a launch configuration for speed; - use reductions, scans, atomics, and streams with confidence; - reach for the right library - and verify its result; >The GPU stops being a black box. Go build something that needed all those threads.

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Pages: 580, Paperback, Independently published


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  • 9798183777802
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