Programming with Context-Sensitive Holes using Dependency-Aware Tuning

TL;DR

This paper presents a new methodology for writing programs with decision variables called holes, which are expanded and tuned automatically using context sensitivity and dependency analysis to improve software performance and maintainability.

Contribution

It introduces context-sensitive holes and dependency-aware tuning, enabling automatic and efficient decision-making in program development without extensive code rewriting.

Findings

Effective automatic tuning of algorithm and data structure choices

Reduction of search space through static dependency analysis

Successful implementation in the Miking system

Abstract

Developing efficient and maintainable software systems is both hard and time consuming. In particular, non-functional performance requirements involve many design and implementation decisions that can be difficult to take early during system development. Choices -- such as selection of data structures or where and how to parallelize code -- typically require extensive manual tuning that is both time consuming and error-prone. Although various auto-tuning approaches exist, they are either specialized for certain domains or require extensive code rewriting to work for different contexts in the code. In this paper, we introduce a new methodology for writing programs with holes, that is, decision variables explicitly stated in the program code that enable developers to postpone decisions during development. We introduce and evaluate two novel ideas: (i) context-sensitive holes that are expanded by the compiler into sets of decision variables for automatic tuning, and (ii) dependency-aware tuning, where static analysis reduces the search space by finding the set of decision variables that can be tuned independently of each other. We evaluate the two new concepts in a system called Miking, where we show how the general methodology can be used for automatic algorithm selection, data structure decisions, and parallelization choices.