Open Source Heterogeneous Programming for Python* Developers

There are limited heterogeneous computing opportunities for Python* developers. Data Parallel Extensions for Python* language addresses this issue by bringing the power of SYCL* to Python users. The extensions extend numerical Python capabilities beyond CPUs, enabling high-performance gains on data parallel devices like GPUs.

This session walks you through how to use the extensions, ultimately enabling you to offload Python data and workloads to any SYCL device, such as GPUs, with little code effort.

This session shows how to:

• Use the extensions for open source heterogeneous computing and compilation.
• Write SYCL kernels in Python.
• Use a just-in-time (JIT) compilation in Python on any SYCL device for near-native performance
• Achieve data interoperability and scale via powerful drop-in replacements for NumPy and Numba*.

The session includes technical demos that showcase the Data Parallel Extensions for Python language in action, including the speedups at every step.

Skill level: Intermediate

• Download Data Parallel Extensions for Python Language (GitHub*)
• Demos are done on the Intel® Tiber™ AI Cloud. If you don’t have an account, get an introductory one for free.

• Pairwise Distance: Open Source XPU Programming with Data Parallel Extensions for Python Language

See All Code Samples