Pitfalls of Unified Memory Models in GPUs

What's the focus of your work these days?

I spend most of my time trying to understand mathematical objects and algorithms before implementing them as efficiently as I possibly can across different hardware. This work spans across all levels of the computing hierarchy.

What's the motivation for your talk at QCon London 2024?

To help the audience understand how unified memory can both help and hinder software development, as well as the tools that can be used to profile and understand the code that runs on their GPUs.

How would you describe your main persona and target audience for this session?

There will be something in this talk for a range of personas, provided that they're interested in GPUs. The talk will cover the high-level ideas behind how a GPU works before we dive into the deep details. This talk will likely not appeal to highly-experienced GPU programmers. I would like to reach an audience that
is curious about how programming a GPU is different compared to programming for a CPU.

Is there anything specific that you'd like people to walk away with after watching your session?

Software engineering is a constant balance between complexity and performance. I would like everyone to walk away with the knowledge that this relationship is not necessarily linear; indeed, we can sometimes reduce complexity without harming the overall performance of our software. On the other hand, it is sometimes necessary to make our programs slightly more complicated in order to achieve peak performance.