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.
- 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.
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.