A Domain-Extensible Compiler with Controllable Automation of Optimisations
Thomas Koehler

TL;DR
This paper presents the Shine compiler, which enhances domain-extensibility and controllable automation to optimize high-performance code for heterogeneous architectures, bridging automation and manual tuning challenges.
Contribution
It introduces new code generation features, demonstrates domain-specific optimizations, and proposes sketch-guided equality saturation for semi-automated program rewriting.
Findings
Enhanced Shine compiler with new code generation features
Successful optimization of image processing pipeline for corner detection
Introduction of sketch-guided equality saturation for guided program rewriting
Abstract
In high performance domains like image processing, physics simulation or machine learning, program performance is critical. Programmers called performance engineers are responsible for the challenging task of optimising programs. Two major challenges prevent modern compilers targeting heterogeneous architectures from reliably automating optimisation. First, domain-specific compilers such as Halide for image processing and TVM for machine learning are difficult to extend with the new optimisations required by new algorithms and hardware. Second, automatic optimisation is often unable to achieve the required performance, and performance engineers often fall back to painstaking manual optimisation. This thesis shows the potential of the Shine compiler to achieve domain-extensibility, controllable automation, and generate high performance code. Domain-extensibility facilitates adapting…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Cloud Computing and Resource Management · Software Engineering Research
