Opportunistically Parallel Lambda Calculus

Scripting languages are widely used to compose external calls such as native libraries and network services. In such scripts, execution time is often dominated by waiting for these external calls, rendering traditional single-language optimizations ineffective. To address this, we propose a novel op...

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Veröffentlicht in:Proceedings of ACM on programming languages Jg. 9; H. OOPSLA2; S. 2596 - 2622
Hauptverfasser: Mell, Stephen, Kallas, Konstantinos, Zdancewic, Steve, Bastani, Osbert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 09.10.2025
Schlagworte:
Parallel programming languages
Software and its engineering
LLM agents
scripting
streaming
parallelization
concurrency
ISSN:2475-1421, 2475-1421
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Zusammenfassung:Scripting languages are widely used to compose external calls such as native libraries and network services. In such scripts, execution time is often dominated by waiting for these external calls, rendering traditional single-language optimizations ineffective. To address this, we propose a novel opportunistic evaluation strategy for scripting languages based on a core lambda calculus that automatically dispatches independent external calls in parallel and streams their results. We prove that our approach is confluent, ensuring that it preserves the programmer’s original intent, and that it eventually executes every external call. We implement this approach in a scripting language called Opal. We demonstrate the versatility and performance of Opal, focusing on programs that invoke heavy external computation through the use of large language models (LLMs) and other APIs. Across five scripts, we compare to several state-of-the-art baselines and show that opportunistic evaluation improves total running time (up to 6.2×) and latency (up to 12.7×) compared to standard sequential Python, while performing very close (between 1.3% and 18.5% running time overhead) to hand-tuned manually optimized asynchronous Rust. For Tree-of-Thoughts, a prominent LLM reasoning approach, we achieve a 6.2× performance improvement over the authors’ own implementation.
ISSN:2475-1421
2475-1421
DOI:10.1145/3763143