Distill: Domain-Specific Compilation for Cognitive Models

TL;DR

Distill is a domain-specific compilation tool that transforms Python-based cognitive models into LLVM IR, significantly improving execution speed and enabling analysis of data flow properties for cognitive science research.

Contribution

It introduces a novel LLVM-based compilation approach tailored for cognitive models, balancing Python flexibility with high-performance execution.

Findings

Significantly faster execution of cognitive models.

Enables data flow analysis for cognitive science.

Open-source tool adopted by researchers.

Abstract

This paper discusses our proposal and implementation of Distill, a domain-specific compilation tool based on LLVM to accelerate cognitive models. Cognitive models explain the process of cognitive function and offer a path to human-like artificial intelligence. However, cognitive modeling is laborious, requiring composition of many types of computational tasks, and suffers from poor performance as it relies on high-level languages like Python. In order to continue enjoying the flexibility of Python while achieving high performance, Distill uses domain-specific knowledge to compile Python-based cognitive models into LLVM IR, carefully stripping away features like dynamic typing and memory management that add overheads to the actual model. As we show, this permits significantly faster model execution. We also show that the code so generated enables using classical compiler data flow analysis passes to reveal properties about data flow in cognitive models that are useful to cognitive scientists. Distill is publicly available, is being used by researchers in cognitive science, and has led to patches that are currently being evaluated for integration into mainline LLVM.