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Laminar: A New Serverless Stream-Based Framework with Semantic Code Search and Code Completion

Workshop: The 18th Workshop on Workflows in Support of Large-Scale Science (WORKS23) - Part 1 of 2

Authors: Zaynab Zahra, Zihao Li, and Rosa Filgueira (University of St Andrews, Scotland)

Abstract: This paper introduces Laminar, a novel serverless framework based on dispel4py, a parallel stream-based dataflow library. Laminar efficiently manages streaming workflows and components through a dedicated registry, offering a seamless serverless experience. Leveraging large language models, Laminar enhances the framework with semantic code search, code summarization, and code completion. This contribution enhances serverless computing by simplifying the execution of streaming computations, managing data streams more efficiently, and offering a valuable tool for both researchers and practitioners.

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