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Data-Driven Dynamic Algorithm Dispatch with Large Language Models

A new arXiv paper introduces an approach using LLaMA 3 and prompt engineering to generate dynamic algorithmic dispatch heuristics for high-performance linear algebra.

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Read the source →arXiv cs.AI — Data-Driven Dynamic Algorithm Dispatch with Large Language Models
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A paper submitted to arXiv on August 21, 2026, details a large language model (LLM)-driven method for creating dynamic algorithmic dispatch heuristics in high-performance linear algebra. This work, developed as part of the DARPA-MIT SmartSolve project, combines prompt engineering with LLaMA 3 and a curated performance database.

The model learns to synthesize selection heuristics by exploiting structural patterns to identify fast algorithmic choices. A case study on LU factorization demonstrates the model's ability to replicate expert-designed strategies.

Key Points

  • The approach uses LLaMA 3 combined with prompt engineering.
  • A curated performance database informs the model's learning process.
  • The model synthesizes selection heuristics that exploit structural patterns.
  • These heuristics identify fast algorithmic choices in linear algebra.
  • A case study on LU factorization replicated expert-designed strategies.
  • The work was developed as part of the DARPA-MIT SmartSolve project.
  • The paper was accepted at the 2025 IEEE High Performance Extreme Computing Conference (HPEC).
  • It received an Outstanding Short Paper Award.

Context

According to the authors, the method focuses on generating dynamic algorithmic dispatch heuristics. This involves teaching an LLM to recognize structural patterns within linear algebra problems and then select the most efficient algorithms based on those patterns. The LU factorization case study served as a demonstration of the model's capability to reproduce strategies typically designed by human experts. The paper was submitted by Rushil Shah, Emmanuel Lujan, Rabab Alomairy, and Alan Edelman.

Why It Matters

This research indicates a potential shift in how high-performance linear algebra software can be developed, moving towards more adaptive and faster systems. For builders and researchers, it highlights a method for algorithmic discovery that leverages LLMs to automate and optimize algorithm selection, potentially reducing the need for manual expert intervention in certain computational tasks.

What To Do

  • Review the paper's abstract on arXiv to understand the core methodology.
  • Note the use of LLaMA 3 and prompt engineering as a specific technique.
  • Examine the LU factorization case study for insights into the model's application.
  • Consider the implications of LLM-driven algorithmic discovery for future software development in linear algebra.

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/atlas/llama-open /techniques/prompt-engineering