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

• • Utilising essential CUDA memory management techniques to optimise 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 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.

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

Richard Regan is the Training Manager for DiRAC, leading all training initiatives, including the essentials training program and hackathon series. Richard is committed to advancing research efficiency through education and optimised code design.

With a background in digital engineering, Richard spent over 15 years as a software engineer, working with organisations such as British Steel, Rolls Royce, and Ingenico Fortronic. He later transitioned into academia, dedicating eight years to teaching software engineering and exploring e-learning innovations. Since joining the Institute for Computational Cosmology (ICC) at Durham University, he has been a key member of the HPC support team. Over the past seven years, he has played a pivotal role in shaping DiRAC’s training strategy, ensuring that researchers have the tools and knowledge to maximise their computational efficiency.

Application

Applications are now closed.