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SKU/Artículo: AMZ-B0G47XSP56
High-Performance GPU Computing with Python: Unlock Massive Speedups in Data Science and ML Using CUDA and Numba
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.74 kg
0.74 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Unlock 1000x Performance Gains, Without Leaving PythonIf your Python code is slowing you down, you’re not alone. Modern datasets, simulations, and AI workloads demand more speed than CPUs alone can provide. This book gives you the missing piece: the raw, massively parallel power of GPUs—made accessible directly from Python.What This Book Allows You to DoIdentify real performance bottlenecks in your Python codeRun NumPy-style computation directly on the GPUWrite custom CUDA kernels in pure Python using NumbaProfile, optimize, and scale your GPU applicationsAchieve real-world speedups in image processing, simulations, ML, and moreAbout the Technology GPUs excel at data-parallel computation, processing millions of independent operations simultaneously. With modern tools like CuPy, Numba, Nsight Systems, cuBLAS, cuFFT, and RAPIDS, you can now unleash this power without switching to C++ or mastering low-level CUDA. This book shows you exactly how.Book Summary High-Performance GPU Programming with Python and CUDA bridges the gap between friendly Python code and high-performance GPU computation. You’ll start by understanding why Python is slow for large-scale numerical work and learn how to profile your code to find the true bottlenecks. Then, step by step, you’ll port that code to the GPU—first with drop-in CuPy acceleration, then with fully custom CUDA kernels using Numba.Across practical examples—grayscale image filtering, K-Means clustering, Monte Carlo simulations, and real-time video processing—you’ll follow the same cycle used by professional HPC developers: profile → accelerate → optimize. By the end, you’ll not only write fast GPU code—you’ll think in parallel.What’s Inside This Book?CuPy as a NumPy-compatible GPU acceleratorWriting and launching custom kernels with NumbaUnderstanding grids, blocks, threads & the CUDA execution modelManaging memory transfers and avoiding GPU bottlenecksProfiling with Nsight Systems for real optimizationShared memory, tiling, streams & pipelined executionFull case studies in finance, image processing, and MLWhen to use RAPIDS, cuBLAS, cuFFT, and PyCUDA About the Reader This book is for Python developers, data scientists, ML/AI engineers, quants, and researchers who know Python well and want faster performance, without switching languages. No prior CUDA experience required.Ready to turn your CPU-bound code into GPU-accelerated powerhouses? Start reading High-Performance GPU Programming with Python and CUDA and unlock the speed hiding inside your machine today.
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U$S 72,48
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