Learning Algorithms for Automatic Data Structure Design

Abstract

We present practical learning-based search algorithms that make it possible to automatically design key-value data structures. Our work allows searching within a space of 10100 possible designs for the optimal data structure. The input is a workload specification and the output is an abstract syntax tree which can be absorbed by modern code-generation techniques. Given the vast design space our solution is based on learning and we show that it can find the close to optimal data structure design in a matter of a few seconds.