Stateless model checking is a powerful technique for testing concurrent programs, but suffers from exponential state space explosion when the test input parameters are too large. Several reduction techniques can mitigate this explosion, but even after pruning equivalent interleavings, the state space size is often intractable. Most prior tools are limited to preempting only on synchronization APIs, which reduces the space further, but can miss unsynchronized thread communication bugs. Data race detection, another concurrency testing approach, focuses on suspicious memory access pairs during a single test execution. It avoids concerns of state space size, but may report races that do not lead to observable failures, which jeopardizes a user’s willingness to use the analysis.

Multithreaded programs are hard to get right. A key reason is that the contract between developers and runtimes grants exponentially many schedules to the runtimes. We present Parrot, a simple, practical runtime with a new contract to developers. By default, it orders thread synchronizations in the well-defined round-robin order, vastly reducing schedules to provide determinism (more precisely, deterministic synchronizations) and stability (i.e., robustness against input or code perturbations, a more useful property than determinism). When default schedules are slow, it allows developers to write intuitive performance hints in their code to switch or add schedules for speed. We believe this "meet in the middle" contract eases writing correct, efficient programs.

Rosetta is one of the leading algorithms for protein structure prediction today. It is a Monte Carlo energy minimization method requiring many random restarts to find structures with low energy. In this paper we present a resampling technique for structure prediction of small alpha/beta proteins using Rosetta. From an ini- tial round of Rosetta sampling, we learn properties of the energy landscape that guide a subsequent round of sampling toward lower-energy structures. Rather than attempt to fit the full energy landscape, we use feature selection methods—both L1-regularized linear regression and decision trees—to identify structural features that give rise to low energy. We then enrich these structural features in the second sampling round. Results are presented across a benchmark set of nine small al- pha/beta proteins demonstrating that our methods seldom impair, and frequently improve, Rosetta’s performance.

Structured game representations have recently attracted interest as models for multi-agent artificial intelligence scenarios, with rational behavior most commonly characterized by Nash equilibria. This paper presents efficient, exact algorithms for computing Nash equilibria in structured game representations, including both graphical games and multi-agent influence diagrams (MAIDs). The algorithms are derived from a continuation method for normal-form and extensive-form games due to Govindan and Wilson; they follow a trajectory through a space of perturbed games and their equilibria, exploiting game structure through fast computation of the Jacobian of the payoff function. They are theoretically guaranteed to find at least one equilibrium of the game, and may find more. Our approach provides the first efficient algorithm for computing exact equilibria in graphical games with arbitrary topology, and the first algorithm to exploit fine-grained structural properties of MAIDs. Experimental results are presented demonstrating the effectiveness of the algorithms and comparing them to predecessors. The running time of the graphical game algorithm is similar to, and often better than, the running time of previous approximate algorithms. The algorithm for MAIDs can effectively solve games that are much larger than those solvable by previous methods.