CuPy Tutorial Guides Python Developers Through GPU Acceleration, Custom CUDA Kernels and Profiling

A hands — on tutorial shows how to use CuPy as a GPU-accelerated alternative to NumPy, with examples that demonstrate setup, benchmarking, custom CUDA kernels and profiling techniques for high-performance Python computing.

A hands — on tutorial demonstrates how CuPy can accelerate numerical Python workloads on NVIDIA GPUs, opening a path from familiar NumPy code to GPU performance. It begins with GPU introspection code that queries device properties and CUDA runtime details so users can verify both CuPy and hardware state before running heavy computations. The setup snippet attempts a CuPy import and falls back to pip installing cupy-cuda12x, giving a practical first step for reproducible environments.

The tutorial provides timed comparisons of NumPy versus CuPy using concrete benchmarks: a 4096×4096 matrix multiplication and an FFT on 2^21 complex values. Those workloads are measured by a provided bench function that handles warmup runs and stream synchronization to ensure fair timing, making it clear where CuPy yields speedups and how to measure them reliably.

Beyond basic array operations, the guide covers advanced CUDA-level features accessible from Python. Topics include memory pools, custom elementwise and reduction kernels and raw CUDA kernels, CUDA streams and events, sparse matrices and dense linear solvers, GPU image processing via cupyx.scipy, DLPack interoperability, cupyx.jit and kernel fusion, and event — based profiling to analyze performance bottlenecks.

The tutorial is practical in orientation: it aims to give developers a working path from NumPy — style code to low-level CUDA performance, showing how to identify where GPU acceleration pays off and how to profile and tune kernels. Examples of interoperability and profiling are positioned to ease integrating CuPy into larger machine — learning or HPC toolchains and to support targeted optimization for production workloads.