High-Performance GPU Computing with Python: Unlock Massive Speedups in Data Science and ML Using CUDA and Numba

Unlock 1000x Performance Gains, Without Leaving Python If 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 Do Identify real performance bottlenecks in your Python code Run NumPy-style computation directly on the GPU Write custom CUDA kernels in pure Python using Numba Profile, optimize, and scale your GPU applications Achieve real-world speedups in image processing, simulations, ML, and more About 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 accelerator Writing and launching custom kernels with Numba Understanding grids, blocks, threads & the CUDA execution model Managing memory transfers and avoiding GPU bottlenecks Profiling with Nsight Systems for real optimization Shared memory, tiling, streams & pipelined execution Full case studies in finance, image processing, and ML When 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. View Less