III: Small: Declarative Recursive Computation on a Database System

William Marsh Rice University, Houston TX

Investigators

Abstract

Machine learning (ML) will have a huge economic and scientific impact over the upcoming decades. But despite the growing importance of ML, there has been relatively little work focused on asking what systems to support machine learning should look like. The result is that it is difficult to apply ML to non-standard cases: Big Data, large or complex models that take a long time to train or require more RAM than is available on a graphics processing unit (GPU), or learning problems with hard training time constraints, to name a few. The proposed project aims to address such deficiencies by applying ideas from relational database systems to the design and implementation of systems for ML. Building an ML system on top of a relational-style engine will enable the design of ML systems that are able to automatically generate compute plans for specific ML tasks with little programmer effort. Those plans will be optimized and executed to match the data size, layout, and the compute hardware. The code to implement an ML algorithm will be the same no matter whether the computation is run on a local machine, or in a distributed environment. If successful, the project will radically expand the ease-of-use and applicability of ML. There are a number of technical questions that need to be answered for ML computations to be run on top of a relational system, and answering such questions will be at the heart of the project. For example: How can ML primitives (convolutions, recurrent modules, etc.) be mapped onto relational primitives? How can large objects (matrices/tensors) be chucked into records so relational implementations run efficiently? And since a developer of ML algorithms is unlikely to accept SQL as a programming language: How to translate Karas-like Python programs into relational algebra? This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.