Course on CUDA Programming on NVIDIA GPUs, July 24-28, 2023
This year the course will be taught by myself and
Prof. Wes Armour
who is the PI for
the first national GPU supercomputer for Machine Learning.
Online registration should be set up by the end of April, with a link
from this webpage.
This is a one-week hands-on course for students, postdocs, academics
and others who want to learn how to develop applications to run on
NVIDIA GPUs using the CUDA programming environment. All that will
be assumed is some proficiency with C and basic C++ programming.
No prior experience with parallel computing will be assumed.
The course consists of approximately 3 hours of lectures and 4 hours
of practicals each day. The aim is that by the end of the course you
will be able to write relatively simple programs and will be confident
and able to continue learning through studying the examples provided
by NVIDIA on GitHub.
All attendees should bring a laptop to access the GPUs servers
which will be used for the practicals.
The costs for the course are:
Anyone with a status which does not fit into one of the categories above,
including those outside the UK who are not from a university or company,
should contact me to discuss the appropriate fee category.
- free for everyone in Oxford (due to central funding)
- £250 for those from other UK universities
- £500 for those from UK government labs,
UK not-for-profit organisations,
and foreign universities
- £2500 for those from industry
The intention is that these costs should not deter anyone from attending
the course. The higher costs for certain participants correspond to the
fact that they will be paying more for their travel and accommodation,
and/or their organisations will be paying more for their time spent
attending the course. It also reflects the UK funding for the facilities
We think it is much better for people to attend the course in person
so that we can support everyone with the practicals,
but if there is interest in remote attendance, particularly from those
in industry who may be unable to come to Oxford for a week, then we
are open to the possibility -- please discuss with me how best we could
support you with the practicals.
The lectures and practicals will all take place in the
Attendees should bring laptops for accessing the remote servers to carry
out the practicals. It would be good to use fully-charged laptops, but we
will try to provide adequate charging points as far as possible.
Travel to Oxford
For those coming to Oxford, especially from abroad, there is travel advice
Accommodation and food
Those attending the course must arrange their own accommodation.
These three are within a few minutes walk, and are arranged in order
of increasing cost:
Alternatively, you might consider using
For coffee, breakfast and lunch, there is a good cafe in the
basement of the Mathematical Institute. Little Clarendon Street,
which is nearby, has several restaurants for dinner
(and an excellent ice cream shop), and there are two sandwich
shops for lunch on either side of its junction with Woodstock
Road (A4144 on Google Maps). The Lamb & Flag and
The Eagle & Child are two popular pubs in St. Giles
(the continuation of the A4144).
For the first three days we will follow this timetable:
On the last two days we will switch to having both lectures in the morning,
and then have practicals all afternoon. This provides more time for longer
practicals, and will also allow those coming to Oxford from far away to
leave when they wish on Friday afternoon.
- 09:15 - 10:45 lecture
- 10:45 - 11:15 break
- 11:15 - 12:45 practical
- 12:45 - 14:00 lunch break
- 14:00 - 15:30 lecture
- 15:30 - 16:00 break
- 16:00 - 17:30 practical
Please read chapters 1 and 2 of the NVIDIA CUDA C Programming Guide
which is available both as
CUDA is an extension of C/C++, so if you are a little rusty with C/C++
you should refresh your memory of it.
Most attendees will be provided with accounts on the
system which has a number of NVIDIA GPU nodes.
Before starting the practicals, please read these
Some details on the Slurm batch queueing system are available
Those with accounts on JADE
may prefer to use it for their practicals.
The practicals all use these header files
which came originally from the CUDA SDK. They provide routines for
error-checking and initialisation.
Tar files for all practicals
Application: a trivial "hello world" example
CUDA aspects: launching a kernel, copying data to/from the graphics card,
error checking and printing from kernel code
Note: the instructions explain how files can be copied from my user account
so there's no need to download from here
Application: Monte Carlo simulation using NVIDIA's CURAND library
for random number generation
CUDA aspects: constant memory, random number generation, kernel timing,
minimising device memory bandwidth requirements
Application: 3D Laplace finite difference solver
CUDA aspects: thread block size optimisation, multi-dimensional memory layout
CUDA aspects: dynamic shared memory, thread synchronisation
Application: using the CUBLAS and CUFFT libraries
Application: revisiting the simple "hello world" example
CUDA aspects: using g++ for the main code, building libraries,
Application: tri-diagonal equations
Application: scan operation and recurrence equations
Application: pattern matching
Application: streams and OpenMP multithreading
Application: more on streams and overlapping computation and communication
Many thanks to:
- the Mathematical Institute for hosting the lectures and practicals
- Oxford's Advanced Research Computing for the GPU servers used
in the practicals
- JADE for the GPU servers used
in the practicals
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