Probabilistic Concurrent Reasoning in Outcome Logic: Independence, Conditioning, and Invariants

Abstract

Although randomization has long been used in distributed computing, formal methods for reasoning about probabilistic concurrent programs have lagged behind. No existing program logics can express specifications about the full distributions of outcomes resulting from programs that are both probabilistic and concurrent. To address this, we introduce Probabilistic Concurrent Outcome Logic (pcOL), which incorporates ideas from concurrent and probabilistic separation logics into Outcome Logic to introduce new compositional reasoning principles. At its core, pcOL reinterprets the rules of Concurrent Separation Logic in a setting where separation models probabilistic independence, so as to compositionally describe joint distributions over variables in concurrent threads. Reasoning about outcomes also proves crucial, as case analysis is often necessary to derive precise information about threads that rely on randomized shared state. We demonstrate pcOL on a variety of examples, including to prove almost sure termination of unbounded loops.