Juggling GPU tasks with asyncio
Getting peak performance with a GPU requires juggling concurrent tasks: copying data to the GPU, processing data, and copying results back off can all happen in parallel. In a distributed system, data arrives from the network and results are sent back over the network. Python's asyncio module is a great way to manage all these concurrent tasks while avoiding many of the hazards of multiple threads.
This talk will describe how I've used asyncio (actually trollius, the Python 2 backport) to make this all work for GPU-accelerated real-time processing in the MeerKAT radio telescope. I'll cover some helper classes I've written for ensuring that operations happen in the right order, and talk about how changing from a threaded model to trollius has simplified the code.
No experience with GPU programming or asyncio/trollius is required or expected. Some prior exposure to event-driven programming or coroutines in Python would be useful.
