Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML

2026-04-19

AI Summary

This work addresses the lack of a universal, flexible, and cluster-agnostic workload representation in existing distributed machine learning systems, which hinders efficient design space exploration. To overcome this limitation, the paper introduces Flint, a novel framework that leverages the intermediate representation of machine learning compilers to extract workload graphs for clusters of arbitrary scale—without requiring actual hardware execution. By decoupling workload modeling from underlying hardware specifics and validating accuracy through execution traces, Flint ensures both fidelity and portability. Experimental results demonstrate that Flint effectively enables flexible and efficient design space exploration while substantially reducing evaluation overhead.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Design space exploration for future distributed Machine Learning systems suffers from a lack of readily available workload representation that enables flexible exploration across the stack. We present Flint, a framework that bridges this gap by leveraging the Intermediate Representation of Machine Learning framework compilers. The compiler does the heavy weight lifting of understanding and preserving the behavior of the original model code. Flint can collect the workload representation of arbitrary cluster size because it interfaces with the compiler before hardware execution. We validate the workload graph against post-execution traces and show the flexibility of Flint through a design space exploration case study.

Problem

• design space exploration
• distributed machine learning
• workload representation
• compiler intermediate representation

Innovation

• compiler-enabled
• cluster-free
• design space exploration
• intermediate representation
• distributed machine learning