2026-08-12 –, Room 2
In this talk, we present the current state of our static DAG optimization and scheduling package ComputableDAGs.jl. The package allows to represent computations as static computational graphs. These graphs can be procedurally generated, automatically analyzed and optimized, and finally scheduled, and executed with no runtime overhead. The optimization can exploit domain specific knowledge about the computational problem, provided through meta information on the graph, without requiring an actual domain-specific language. Depending on available hardware, parts of the graph are automatically scheduled to accelerator devices if possible.
We present the current capabilities and design of the package, using a high-energy physics application as a case study. Furthermore, we report about ongoing challenges, and invite discussions about usability and improvements.
I completed my computer science degree at a master's level in 2024 and am now a PhD student at the Helmholtz-Zentrum Dresden-Rossendorf in the same field, with a focus on physics applications. My work is part of the ongoing democratizing models project.