Explore contemporary techniques for maximizing application speed, covering everything from compiler optimization and efficient concurrency to advanced profiling tools and front-end rendering performance.
From this track
Navigating the Edge of Scale and Speed for Physics Discovery
Wednesday Mar 18 / 10:35AM GMT
Details coming soon.
Thea Klaeboe Aarrestad
Particle Physics and Real-Time ML @CERN @ETH Zürich
Vector Search on Columnar Storage
Wednesday Mar 18 / 11:45AM GMT
Managing vector data entails storing, updating, and searching collections of large and multi-dimensional pieces of data. Some believe that this justifies the creation of a new class of data systems specialized for this.
Peter Boncz
Professor @CWI, Co-Creator of MonetDB, VectorWise and MotherDuck, Database Systems Researcher, and Entrepreneur
Not Just I/O: Using Async/Await for Computational Scheduling
Wednesday Mar 18 / 01:35PM GMT
In the past two years I have developed a new query execution engine for Polars, which not only tries to execute as much of your query in parallel as possible, but in a streaming fashion as well, such that you can process data sets which do not fit in memory.
Orson Peters
Senior Engineer of Query Execution @Polars, (Co-)Author of Stdlib Sort in Rust & Go
Looking Under the Hood: Data Processing Systems Performance Tricks (and How to Apply Them to Your Code)
Wednesday Mar 18 / 02:45PM GMT
Modern data processing systems—databases, analytics engines, vector stores, and stream processors—hide an extraordinary amount of performance engineering beneath their abstractions.
Holger Pirk
Associate Professor for Data Management Systems at Imperial College London and Avid Runner — Minimizing Cache Misses, Thread Divergence and Aerobic Decoupling
Automatically Retrofitting JIT Compilers
Wednesday Mar 18 / 03:55PM GMT
We as a community have attempted, multiple times, to speed up languages such as Lua, Python, and Ruby by hand-writing JIT compilers. Sometimes we've had short-term success, but the size, and pace of change, of their standard implementations has proven difficult to keep up with over time.
Laurence Tratt
Shopify / Royal Academy of Engineering Research Chair in Language Engineering @King's College London
