The Data Parallel Essentials for Python learning path series demonstrates high-performing code targeting Intel XPUs using Python. The three-part series will introduce Numba-dppy and show examples of how to write data-parallel code inside @numba.jit decorated and @kernel decorator functions to offload them to a SYCL device. The series will also cover dpctl, a companion library intended to make it easier to write Python native extensions based on SYCL. Numba-dppy is packaged as part of Intel Distribution for Python*, which is included with the Intel oneAPI AI Analytics Toolkit.
For use cases, the series will cover Pairwise, Black Scholes, K-Means, and GPairs as examples to demonstrate the CPU and GPU implementation of numba-dppy and practice live sample code on the Intel DevCloud and/or resources at Argonne's Joint Laboratory for System Evaluation (JLSE).