Course on CUDA Programming on NVIDIA GPUs, July 23--27, 2012

We are now ready for online bookings here.

This is a 5-day 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 as part of their SDK (software development kit).

This course is open to all -- to register please go to the registration webpage

Costs for the course will be as follows: An online registration page will be provided soon.

EPSRC-supported PhD students may be able to get assistance with travel and accommodation costs from the EPSRC Coordinated HPC Training Centre.

Venue

The course is being organised by OeRC but is being held in the Engineering Science department Thom building -- please come to the main entrance on the first floor (go up the external stairs to reach an elevated walkway linking several buildings) on Monday morning at 9:00.

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: The location for the lectures and practicals is marked as A on this Google map. Little Clarendon Street, which is towards the left side of the map, has several restaurants for dinner, and there are two sandwich shops for lunch on either side of its junction with the road marked as the A4144. Towards the bottom of the map, the Lamb & Flag and The Eagle & Child are two popular pubs.

In addition, there is an Engineering Science common room nearby (turn left as you leave the Thom building and go straight ahead into the first building) which is open 9-2 and serves sandwiches, pizza and other snacks.

Timetable

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 will also allow those coming to Oxford from far away to leave when they wish on Friday afternoon.

Preliminary Reading

Please bring a printed copy of the NVIDIA CUDA C Programming Guide version 4.1 and have read chapters 1 and 2.

CUDA is an extension of C/C++, so if you are a little rusty with C/C++ you should refresh your memory of it.

Please also look at lecture 0 (4 slides per page for printing) which gives an overview of trends in computing and explains why I believe GPU computing is an important direction for scientific computing for the next 5-10 years.

Additional References


The lectures and practicals below were for the course in 2011 -- they will be revised for 2012.

Lectures

Additional NVIDIA presentations:

Code generator for case study 2:

Practicals

The practicals will be held in the computer teaching laboratory on the 6th floor of the Thom building.

We will be working under Linux, and because of the large number of people taking the course we will be using two different GPU clusters:

The practicals all use this cutil_inline.h header file which is based on one in the CUDA SDK.

Practical 1

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

Practical 2

Application: Monte Carlo simulation using NAG's RNG library

CUDA aspects: constant memory, random number generation, kernel timing, minimising device memory bandwidth requirements

Practical 3

Application: 3D Laplace and ADI finite difference solvers

CUDA aspects: array padding and thread block size optimisation

Practical 4

Application: reduction

CUDA aspects: dynamic shared memory, thread synchronisation

Practical 5

Application: using the CUBLAS and CUFFT libraries

Practical 6

Application: revisiting the simple "hello world" example

CUDA aspects: using g++ for the main code, building libraries, using templates

Practical 7

Application: tri-diagonal equations

Practical 8

Application: scan operation and recurrence equations

Practical 9

Application: pattern matching

Practical 10

Application: auto-tuning

Acknowledgements

Many thanks to