{"product_id":"9781803245423","title":"GPU-Accelerated Computing with Python 3 and CUDA : From low-level kernels to real-world applications in scientific computing and machine learning","description":"\u003cp\u003eAccelerate your Python code on the GPU using CUDA, Numba, and modern libraries to solve real-world problems faster and more efficiently.\u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eBuild a solid foundation in CUDA with Python, from kernel design to execution and debugging\n\u003cbr\u003eOptimize GPU performance with efficient memory access, CUDA streams, and multi-GPU scaling\n\u003cbr\u003eUse JAX, CuPy, RAPIDS, and Numba to accelerate numerical computing and machine learning\n\u003cbr\u003eCreate practical GPU applications, from PDE solvers to image processing and transformers\u003c\/p\u003e\n\n\u003cp\u003eBook DescriptionWriting high-performance Python code doesn't have to mean switching to C++. This book shows you how to accelerate Python applications using NVIDIA's CUDA platform and a modern ecosystem of Python tools and libraries. Aimed at researchers, engineers, and data scientists, it offers a practical yet deep understanding of GPU programming and how to fully exploit modern GPU hardware.\u003c\/p\u003e\n\n\u003cp\u003eYou'll begin with the fundamentals of CUDA programming in Python using Numba-CUDA, learning how GPUs work and how to write, execute, and debug custom GPU kernels. Building on this foundation, the book explores memory access optimization, asynchronous execution with CUDA streams, and multi-GPU scaling using Dask-CUDA. Performance analysis and tuning are emphasized throughout, using NVIDIA Nsight profilers.\u003c\/p\u003e\n\n\u003cp\u003eYou'll also learn to use high-level GPU libraries such as JAX, CuPy, and RAPIDS to accelerate numerical Python workflows with minimal code changes. These techniques are applied to real-world examples, including PDE solvers, image processing, physical simulations, and transformer models.\u003c\/p\u003e\n\n\u003cp\u003eWritten by experienced GPU practitioners, this hands-on guide emphasizes reproducible workflows using Python 3.10+, CUDA 12.3+, and tools like the Pixi package manager. By the end, you'll have future-ready skills for building scalable GPU applications in Python.What you will learn\u003c\/p\u003e\n\n\u003cp\u003eUnderstand GPU execution, parallelism, and the CUDA programming model\n\u003cbr\u003eWrite, launch, and debug custom CUDA kernels in Python with CUDA\n\u003cbr\u003eProfile GPU code with NVIDIA Nsight and optimize memory access\n\u003cbr\u003eUse CUDA streams and async execution to overlap compute and transfers\n\u003cbr\u003eApply JAX, CuPy, and RAPIDS to numerical computing and machine learning\n\u003cbr\u003eScale GPU workloads across devices using Dask and multi-GPU strategies\n\u003cbr\u003eAccelerate PDE solvers, simulations, and image processing on the GPU\n\u003cbr\u003eBuild, train, and run a transformer model from scratch on the GPU\u003c\/p\u003e\n\n\u003cp\u003eWho this book is forPython developers, (data) scientists, engineers, and researchers looking to accelerate numerical computations without switching to low-level languages. This book is ideal for those with experience in scientific Python (NumPy, Pandas, SciPy) and a basic understanding of computing fundamentals who want deeper control over performance in GPU environments.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":48871591575787,"sku":"EB_CP0800_F24-05_SIMS","price":87.79,"currency_code":"SGD","in_stock":true}],"url":"https:\/\/kinokuniya.com.sg\/products\/9781803245423","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}