Sun 18 Jun 2023 10:20 - 10:40 at Magnolia 18 - EGRAPHS: Optimization

Optimizing a stateful dataflow language is a challenging task. There are strict correctness constraints for preserving properties expected by downstream consumers, a large space of possible optimizations, and complex analyses that must reason about the behavior of the program over time. Classic compiler techniques with specialized optimization passes yield unpredictable performance and have complex correctness proofs. But with e-graphs, we can dramatically simplify the process of building a correct optimizer while yielding more consistent results! In this short paper, we discuss our early work using e-graphs to develop an optimizer for a the Hydroflow dataflow language. Our prototype demonstrates that composing simple, easy-to-prove rewrite rules is sufficient to match techniques in hand-optimized systems.

Sun 18 Jun

Displayed time zone: Eastern Time (US & Canada) change

09:00 - 11:00
09:40
20m
Talk
Automating Constraint-Aware Datapath Optimization using E-Graphs
EGRAPHS
Samuel Coward Imperial College London, UK / Intel Corporation, George A. Constantinides Imperial College London, UK, Theo Drane Intel Corporation, USA
Pre-print File Attached
10:00
20m
Talk
egglog In Practice: Automatically Improving Floating-point Error
EGRAPHS
Oliver Flatt University of Washington, Yihong Zhang University of Washington
10:20
20m
Talk
Optimizing Stateful Dataflow with Local Rewrites
EGRAPHS
Shadaj Laddad University of California at Berkeley, Conor Power University of California at Berkeley, Tyler Hou University of California, Berkeley, Alvin Cheung University of California at Berkeley, Joseph M. Hellerstein University of California, Berkeley
Pre-print File Attached
10:40
20m
Talk
Egg-smol Python: A Pythonic Library for E-graphs
EGRAPHS
Link to publication Pre-print