10 Essential CuPy Techniques for Mastering GPU Computing
Master GPU computing with CuPy through 10 essential techniques including GPU introspection, benchmarking, custom CUDA kernels, streams, sparse matrices, profiling, and more. Complete with practical examples and performance insights.
If you've ever wanted to harness the full power of your GPU for numerical computing in Python, CuPy is the key. This library brings the familiar NumPy syntax to NVIDIA GPUs, but that's just the beginning. Beyond simple array operations, CuPy offers a range of advanced features—from custom kernels to profiling—that can turbocharge your workflows. In this article, we explore ten must-know CuPy techniques, each building on the last, to help you move from CPU-bound code to lightning-fast GPU-accelerated applications.
