A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling

A tutorial shows how to use CuPy for GPU-accelerated Python computing, with NumPy-like syntax and CUDA-backed speedups.

It benchmarks CPU vs. GPU performance and inspects the available GPU hardware before running numerical workloads.

The guide covers custom CUDA kernels, matrix multiplication, FFTs, memory pools, and kernel fusion for better efficiency.

It also explains advanced CuPy features such as CUDA streams, sparse linear algebra, DLPack interoperability, and profiling.