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We present IronSync, an automated verification framework for concurrent code with shared memory. IronSync scales to complex systems by splitting system-wide proofs into isolated concerns such that each can be substantially automated. As a starting point, IronSync’s ownership type system allows a developer to straightforwardly prove both data safety and the logical correctness of thread-local operations. IronSync then introduces the concept of a Localized Transition System, which connects the correctness of local actions to the correctness of the entire system. We demonstrate IronSync by verifying two state-of-the-art concurrent systems comprising thousands of lines: a library for black-box replication on NUMA architectures, and a highly concurrent page cache. 

Building persistent memory (PM) data structures is difficult because crashes interrupt operations, leaving data structures in an inconsistent state. Solving this requires augmenting code that modifies PM state to ensure that interrupted operations can be completed or undone. Today, this is done using careful, hand-crafted code, a compiler pass, or page faults. We propose a new, easy way to transform volatile data structure code to work with PM that uses a cache-coherent accelerator to do this augmentation, and we show that it may outperform existing approaches for building PM structures. 

Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. The guesses come from knowledge gleaned from black box models. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize: when you are having trouble optimizing, just guess.