Programming Language Abstractions for Modularly Verified Distributed Systems

Distributed systems are rarely developed as monolithic programs. Instead, like any software, these systems may consist of multiple program components, which are then compiled separately and linked together. Modern systems also incorporate various services interacting with each other and with client applications. However, state-of-the-art verification tools focus predominantly on verifying standalone, closed-world protocols or systems, thus failing to account for the compositional nature of distributed systems. For example, standalone verification has the drawback that when protocols and their optimized implementations evolve, one must re-verify the entire system from scratch, instead of leveraging compositionality to contain the reverification effort. In this paper, we focus on the challenge of modular verification of distributed systems with respect to high-level protocol invariants as well as for low-level implementation safety properties. We argue that the missing link between the two is a programming paradigm that would allow one to reason about both high-level distributed protocols and low-level implementation primitives in a single verification-friendly framework. Such a link would make it possible to reap the benefits from both the vast body of research in distributed computing, focused on modular protocol decomposition and consistency properties, as well as from the recent advances in program verification, enabling construction of provably correct systems implementations. To showcase the modular verification challenges, we present some typical scenarios of decomposition between a distributed protocol and its implementations. We then describe our ongoing research agenda, in which we are attempting to address the outlined problems by providing a typing discipline and a set of domain-specific primitives for specifying, implementing and verifying distributed systems. Our approach, mechanized within a proof assistant, provides the means of decomposition necessary for modular proofs about distributed protocols and systems

Programming Language Tools and Techniques for 3D Printing

We propose a research agenda to investigate programming language techniques for improving affordable, end-user desktop manufacturing processes such as 3D printing. Our goal is to adapt programming languages tools and extend the decades of research in industrial, high-end CAD/CAM in order to help make affordable desktop manufacturing processes more accurate, fast, reliable, and accessible to end-users. We focus on three major areas where 3D printing can benefit from programming language tools: design synthesis, optimizing compilation, and runtime monitoring. We present preliminary results on synthesizing editable CAD models from difficult-to-edit surface meshes, discuss potential new compilation strategies, and propose runtime monitoring techniques. We conclude by discussing additional near-future directions we intend to pursue

Small Proofs from Congruence Closure

Satisfiability Modulo Theory (SMT) solvers and equality saturation engines must generate proof certificates from e-graph-based congruence closure procedures to enable verification and conflict clause generation. Smaller proof certificates speed up these activities. Though the problem of generating proofs of minimal size is known to be NP-complete, existing proof minimization algorithms for congruence closure generate unnecessarily large proofs and introduce asymptotic overhead over the core congruence closure procedure. In this paper, we introduce an O(n^5) time algorithm which generates optimal proofs under a new relaxed "proof tree size" metric that directly bounds proof size. We then relax this approach further to a practical O(n \log(n)) greedy algorithm which generates small proofs with no asymptotic overhead. We implemented our techniques in the egg equality saturation toolkit, yielding the first certifying equality saturation engine. We show that our greedy approach in egg quickly generates substantially smaller proofs than the state-of-the-art Z3 SMT solver on a corpus of 3760 benchmarks

babble: Learning Better Abstractions with E-Graphs and Anti-Unification

Library learning compresses a given corpus of programs by extracting common structure from the corpus into reusable library functions. Prior work on library learning suffers from two limitations that prevent it from scaling to larger, more complex inputs. First, it explores too many candidate library functions that are not useful for compression. Second, it is not robust to syntactic variation in the input. We propose library learning modulo theory (LLMT), a new library learning algorithm that additionally takes as input an equational theory for a given problem domain. LLMT uses e-graphs and equality saturation to compactly represent the space of programs equivalent modulo the theory, and uses a novel e-graph anti-unification technique to find common patterns in the corpus more directly and efficiently. We implemented LLMT in a tool named BABBLE. Our evaluation shows that BABBLE achieves better compression orders of magnitude faster than the state of the art. We also provide a qualitative evaluation showing that BABBLE learns reusable functions on inputs previously out of reach for library learning.Comment: POPL 202