Quantitative Synthesis for Concurrent Programs

TL;DR

This paper introduces an algorithmic approach for automatically synthesizing concurrent programs that are both correct and optimized for performance based on a parametric model, addressing the challenge of nondeterminism in synchronization.

Contribution

It presents a novel method combining game-theoretic techniques with practical algorithms to synthesize concurrent programs optimized for performance while ensuring correctness.

Findings

The synthesis problem is NEXP-complete.

The proposed algorithm efficiently handles practical concurrent programs.

A prototype tool successfully synthesizes programs for various architectures.

Abstract

We present an algorithmic method for the quantitative, performance-aware synthesis of concurrent programs. The input consists of a nondeterministic partial program and of a parametric performance model. The nondeterminism allows the programmer to omit which (if any) synchronization construct is used at a particular program location. The performance model, specified as a weighted automaton, can capture system architectures by assigning different costs to actions such as locking, context switching, and memory and cache accesses. The quantitative synthesis problem is to automatically resolve the nondeterminism of the partial program so that both correctness is guaranteed and performance is optimal. As is standard for shared memory concurrency, correctness is formalized "specification free", in particular as race freedom or deadlock freedom. For worst-case (average-case) performance, we show that the problem can be reduced to 2-player graph games (with probabilistic transitions) with quantitative objectives. While we show, using game-theoretic methods, that the synthesis problem is NEXP-complete, we present an algorithmic method and an implementation that works efficiently for concurrent programs and performance models of practical interest. We have implemented a prototype tool and used it to synthesize finite-state concurrent programs that exhibit different programming patterns, for several performance models representing different architectures.