优化检索流水线缓存，减少重复计算并统一设计与实现
On Precomputation and Caching in Information Retrieval Experiments with Pipeline Architectures
- 自动缓存通用流水线前缀，避免重复执行
- 通过新扩展包显式缓存操作，提升效率
- 兼顾端到端表达与计算效率，适合研究者使用
现代信息检索系统常采用多组件流水线架构。在研究中，这会导致大量重复计算（如多次查询不同下游重排序器）。为解决此问题，研究者通常使用缓存的“结果”文件作为输入，代表前一阶段输出。但这类文件易出错，造成概念设计与实际实现脱节。为此，本文介绍了在开源 PyTerrier IR 平台中改进缓存能力的最新工作，聚焦两个方向：(1) 比较系统时自动隐式缓存常见流水线前缀；(2) 通过新扩展包 pyterrier-caching 显式缓存操作。该方法既保持流水线端到端表达完整，又避免跨流水线间的冗余计算。
原文摘要 · Abstract (English)
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.
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