The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. DiRAC users have two large GPU clusters they can use, and several small development systems exist in all our sites.
This code camp teaches the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA® and is your first step into accelerating your application with Nvidia GPUs.
The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. This course will teach C/C++ application acceleration using techniques such as:
Upon completion, you’ll be able to accelerate and optimise existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.
Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. No previous knowledge of CUDA programming is assumed.
Athena Elafrou is a graduate of the Electrical and Computer Engineering (ECE) School of the National Technical University of Athens (NTUA). Since October 2020, she has been working as an HPC Consultant at the Research Computing Services (RCS) of the University of Cambridge. She is also pursuing a PhD degree with the parallel systems research group of CSlab@ECE/NTUA under the supervision of Professor Georgios Goumas and holds publications in top-tier journals and conferences in the area of HPC. She is also an NVIDIA DLI Ambassador at the University of Cambridge.
