In this session, we will introduce you to Data Parallel C++ and the importance of performance, portability, and productivity for HPC development. We will set up a Jupyter Lab environment for training, which will allow hands-on compilation and execution of simple DPC++ code samples.

Agenda

• Introduction to Data Parallel C++ (10min)
• Importance of Performance, Portability, and Productivity (10min)
• Setup Jupyter Lab environment for training and hands-on execution of code samples (30min)
• Code walk-thru of matrix multiplication implementation using Math Kernel Library (15min)
• Compile and Execute the same matrix multiplication code sample on CPU and GPU offload (15min)

This module is a part of the Aurora Learning Paths Series.

Presenter

Rakshith Krishnappa is a developer evangelist at Intel, focused on oneAPI, DPC++, and High-Performance Computing. For the last 16 years, he has worked on various Intel products including CPUs, Graphics, GPUs, HPC products, and Software solutions.