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Synthesis of Incremental Linear Algebra Programs
ACM Transactions on Database Systems ( IF 1.8 ) Pub Date : 2020-07-07 , DOI: 10.1145/3385398
Amir Shaikhha 1 , Mohammed Elseidy 2 , Stephan Mihaila 2 , Daniel Espino 2 , Christoph Koch 2
Affiliation  

This article targets the Incremental View Maintenance (IVM) of sophisticated analytics (such as statistical models, machine learning programs, and graph algorithms) expressed as linear algebra programs. We present LAGO, a unified framework for linear algebra that automatically synthesizes efficient incremental trigger programs, thereby freeing the user from error-prone manual derivations, performance tuning, and low-level implementation details. The key technique underlying our framework is abstract interpretation, which is used to infer various properties of analytical programs. These properties give the reasoning power required for the automatic synthesis of efficient incremental triggers. We evaluate the effectiveness of our framework on a wide range of applications from regression models to graph computations.

中文翻译:

增量线性代数程序的综合

本文针对以线性代数程序表示的复杂分析(例如统计模型、机器学习程序和图形算法)的增量视图维护 (IVM)。我们提出了 LAGO,这是一个线性代数的统一框架,它自动合成高效的增量触发程序,从而使用户免于容易出错的手动推导、性能调整和低级实现细节。我们框架的关键技术是抽象解释,它用于推断分析程序的各种属性。这些属性提供了自动合成高效增量触发器所需的推理能力。我们评估了我们的框架在从回归模型到图形计算的广泛应用中的有效性。
更新日期:2020-07-07
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