NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users access to CUDA-X performance for common math operations without disrupting existing workflows. Depending on the API, operations can run on a CPU, CUDA-enabled GPU, or distributed multi-GPU, multi-node systems.
nvmath-python v1.0 release
With the general availability of nvmath-python v1.0, this post explores the library’s design and unique capabilities for accelerating math operations—from a CPU or single GPU up to multi-GPU, multi-node scale. nvmath-python is a Pythonic abstraction layer over the CUDA and NVPL math libraries such as cuFFT, cuBLASLt, cuDSS, cuSPARSE, cuTENSOR, cuBLASMp, and more. A novel approach to sparsity, the universal sparse tensor (UST), enables the user to create their own unique application-optimal sparse format through a domain-specific language without having to implement it in code.
Fast and flexible installation
Installing a Python package with complex native dependencies can be a time-consuming and frustrating experience. nvmath-python installs quickly and can be customized for different environments.
