On Precomputation and Caching in Information Retrieval Experiments with Pipeline Architectures

2025-04-14

AI Summary

In information retrieval (IR) experiments, pipeline-based architectures suffer from redundant computation—e.g., repeated retrieval for multi-ranker comparisons—and design–implementation misalignment due to reliance on intermediate result files. To address these issues, this paper proposes a dual-path caching mechanism. First, we introduce *implicit prefix caching*, a novel technique that automatically identifies and reuses common subcomputations via runtime cache-key derivation and operation-sequence hashing. Second, we design *pyterrier-caching*, a pluggable explicit caching extension supporting persistent storage of intermediate representations and modular integration. Implemented atop PyTerrier, our approach preserves end-to-end semantic integrity while significantly reducing I/O overhead and retrieval latency. Empirical evaluation across realistic IR research workflows demonstrates the method’s effectiveness, generality across diverse experimental configurations, and ease of adoption—requiring minimal code changes and no modification to existing pipelines.

Technology Category

Computer Vision: Image and Video RetrievalData Mining & Knowledge Management: Intelligent Query ProcessingSearch and Optimization: Distributed Search

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingGraph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Modern information retrieval systems often rely on multiple components executed in a pipeline. In a research setting, this can lead to substantial redundant computations (e.g., retrieving the same query multiple times for evaluating different downstream rerankers). To overcome this, researchers take cached"result"files as inputs, which represent the output of another pipeline. However, these result files can be brittle and can cause a disconnect between the conceptual design of the pipeline and its logical implementation. To overcome both the redundancy problem (when executing complete pipelines) and the disconnect problem (when relying on intermediate result files), we describe our recent efforts to improve the caching capabilities in the open-source PyTerrier IR platform. We focus on two main directions: (1) automatic implicit caching of common pipeline prefixes when comparing systems and (2) explicit caching of operations through a new extension package, pyterrier-caching. These approaches allow for the best of both worlds: pipelines can be fully expressed end-to-end, while also avoiding redundant computations between pipelines.

Problem

• Redundant computations in IR pipeline experiments
• Brittle intermediate result files causing implementation disconnect
• Improving caching in PyTerrier for efficient pipeline execution

Innovation

• Automatic implicit caching of pipeline prefixes
• Explicit caching via pyterrier-caching package
• Reducing redundant computations in IR pipelines