Heterogeneous Programming Using Numba-Data Parallel Python* for AI and HPC
Python* has become a useful tool in advancing scientific research and computation with a rich ecosystem of open-source packages for mathematics, science, and engineering. Python is anchored on the performant numerical computation on arrays and matrices, data analysis, and visualization capabilities.
This data parallel Python course demonstrates high-performing code targeting Intel® XPUs using Python. Developers learn how to take advantage of heterogeneous architectures and speed up applications without using low-level proprietary programming APIs.
This course is designed for Python developers who want to learn the basics of data parallel Python (DPPY) for data parallel and heterogeneous hardware (such as CPU and GPU), without leaving the Python ecosystem or compromising on performance.
What will I be able to do?
Practice the essential concepts and features of DPPY with live sample code on the Intel® DevCloud.
Get hands-on practice with code samples in Jupyter* Notebooks running live on Intel DevCloud.