Optimizations on Graph-Level for Domain Specific Computations in Julia and Application to QED

TL;DR

This paper introduces a Julia-based framework for optimizing complex scientific computations by leveraging DAG scheduling and domain-specific information, demonstrated through quantum electrodynamics applications.

Contribution

It presents a novel framework that combines DAG scheduling with domain-specific optimizations in Julia for scientific computations.

Findings

Framework automatically produces optimized code for complex tasks.

Theoretical foundations enable new optimization strategies.

Application to QED demonstrates practical benefits.

Abstract

Complex computational problems in science often consist of smaller parts that can have largely distinct compute requirements from one another. For optimal efficiency, analyzing each subtask and scheduling it on the best-suited hardware would be necessary. Other considerations must be taken into account, too, such as parallelism, dependencies between different subtasks, and data transfer speeds between devices. To achieve this, directed acyclic graphs are often employed to represent these problems and enable utilizing as much hardware as possible on a given machine. In this paper, we present a software framework written in Julia capable of automatically and dynamically producing statically scheduled and compiled code. We lay theoretical foundations and add domain-specific information about the computation to the existing concepts of DAG scheduling, enabling optimizations that would otherwise be impossible. To illustrate the theory we implement an example application: the computation of matrix elements for scattering processes with many external particles in quantum electrodynamics.