Online Event
12 July 9am to 5pm
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.
Overview
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:
- Accelerating CPU-only applications to run their latent parallelism on GPUs
- Utilizing essential CUDA memory management techniques to optimize accelerated applications
- Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
- Leveraging command line and visual profiling to guide and check your work
Upon completion, you’ll be able to accelerate and optimize 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.
Prerequisites
Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. No previous knowledge of CUDA programming is assumed.
Tools, Libraries, and Frameworks Used
- CUDA C++ – https://developer.nvidia.com/cuda-zone
- nvcc – http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html
- Nsight Systems – https://developer.nvidia.com/nsight-systems
About the Instructor
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.
Registering Your Interest
There are a limited number of places available for this course. Your application will be treated as an expression of interest and you are not guaranteed a place at the workshop.
After the application deadline has passed, submissions will be considered, and successful applicants will be offered a place by 8 July.
This event is primarily open to those using one of the DiRAC facilities, but others will be considered if space allows.
REGISTRATION IS CLOSED
