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.