Welcome to the OGHPC Devito-GPU hackathon

The objective of this hackathon is to use Devito's JIT backdoor to transform the code that the Devito compiler generates for two different propagators. In particular:

optimise the code automatically generated for
- OpenMP 5 offloading, or
- OpenACC;
synthesize a new GPU implementation (e.g., CUDA, oneAPI) starting from the sequential version.

Code variants will be benchmarked, and the best performing strategies will be implemented in the Devito compiler. In this way, HPC/GPU developers can contribute to Devito development without having to learn how to develop the compiler itself.

Not able to make it to the OGHPC hackathon in person? No worries - we are a gloablly distributed dev team so everything is already set up to work online :-) At the top of our website, https://www.devitoproject.org/, you will see a link to our Slack workspace. Feel free to browse channels and ask questions - really the key channels for this hackathon are #oghpc and #gpu-dev.

Step 1: Get account and GPU enabled VM for development

You need to click on this link: https://labs.azure.com/register/jltt9mv2

You will be redirected to your Azure Labs VM. There you can set your password for logging in to your VM. You have to wait for a minute or so. When your password is updated, click on the screen emoji at your VM and select Connect via SSH. Copy the ssh command to your terminal and start hacking!

Step 2: Enroll in the hackathon

Simply fork this repo. All forks will be checked periodically.

Step 3: Start hacking!

In the repository that you have just cloned, you will find two Python files. One of these is run-preset.py, which allows you to execute a set of Devito benchmarks that we have pre-selected for this hackathon. Aspects like discretization and duration of the benchmark are fixed, so you won't care about them. The only argument that run-preset.py accepts is the name of the benchmark -- there are two alternatives:

python run-preset.py acoustic: to generate and run an isotropic acoustic forward propagator.
python run-preset.py tti: to generate and run an anisotropic (TTI) acoustic forward propagator.

We suggest to start with the simplest one, acoustic. The first time you run the command, the code gets generated. Instructions will be provided on screen explaining how to hack the generated code. Then, to test your hacking, just re-run the same command. The execution time will be displayed at the end of each run -- look for something along the lines of

Operator `Forward` run in 1.22 s

Where "Forward" is the name of the Operator. If interested in more performance metrics, such as the GFlops/s and GPoints/s performance, just run with the environment variable DEVITO_LOGGING=PERF.

Note: we have configured the VM such that Devito, by default, generates and compiles code with OpenMP GPU-offloading pragmas. This is achieved through the following environment variables:

DEVITO_ARCH=clang
DEVITO_PLATFORM=nvidiaX

If one wants to:

Use a different backend compiler, for example pgcc; then set DEVITO_ARCH=pgcc and unset DEVITO_PLATFORM

Step 4: Push your work

To submit your work at the end of the hackathon, just run python push-files.py. And that's it!

Benchmarking:

Periodically we will:

check all forks for updates
- checkout your updated code;
- run the benchmarks on a dedicated V100;
- update league table [will provide a link to the league table as soon as it is ready].

Tips and hints

We suggest that you always have a terminal running nvtop and/or htop. This can help not only to monitor the GPU usage of your VM but to quickly kill (SIGKILL) an application that is inefficient and save your time.
Feel free to call the members of our team and ask for help if you consider any clarifications or more detailed explanation is needed.

navjotk / starter