Synthesis of Incremental Linear Algebra Programs

Abstract

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.