There is growing interest in termination reasoning for nonlinear programs and, meanwhile, recent dynamic strategies have shown they are able to infer invariants for such challenging programs. These advances led us to hypothesize that perhaps such dynamic strategies for nonlinear invariants could be adapted to learn recurrent sets (for non-termination) and/or ranking functions (for termination). In this paper, we exploit dynamic analysis and draw termination and non-termination as well as static and dynamic strategies closer together in order to tackle nonlinear programs. For termination, our algorithm infers ranking functions from concrete transitive closures, and, for non-termination, the algorithm iteratively collects executions and dynamically learns conditions to refine recurrent sets. Finally, we describe an integrated algorithm that allows these algorithms to mutually inform each other, taking counterexamples from a failed validation in one endeavor and crossing both the static/dynamic and termination/non-termination lines, to create new execution samples for the other one. We have implemented these algorithms in a new tool called DynamiTe. For nonlinear programs, there are currently no SV-COMP termination benchmarks so we created new sets of 38 terminating and 39 non-terminating programs. Our empirical evaluation shows that we can effectively guess (and sometimes even validate) ranking functions and recurrent sets for programs with nonlinear behaviors. Furthermore, we show that counterexamples from one failed validation can be used to generate executions for a dynamic analysis of the opposite property. Although we are focused on nonlinear programs, as a point of comparison, we compare DynamiTe's performance on linear programs with that of the state-of-the-art tool, Ultimate. Although DynamiTe is an order of magnitude slower it is nonetheless somewhat competitive and sometimes finds ranking functions where Ultimate was unable to. Ultimate cannot, however, handle the nonlinear programs in our new benchmark suite.

There is an ongoing effort to provide programming abstractions that ease the burden of exploiting multicore hardware. Many programming abstractions (e.g., concurrent objects, transactional memory, etc) simplify matters, but still involve intricate engineering. We argue that some difficulty of multicore programming can be meliorated through a declarative programming style in which programmers directly express the independence of fragments of sequential programs.In our proposed paradigm, programmers write programs in a familiar, sequential manner, with the added ability to explicitly express the conditions under which code fragments commute. Putting such commutativity conditions into source code offers a new entry point to exploit the known connection between commutativity and parallelism. We give sequential and concurrent semantics for commute statements, as well as a correctness condition called scoped serializability and discuss how it can be enforced when combined with commutativity conditions.We next describe a technique for automatically synthesizing commute conditions directly from source code via a new reduction to commutativity of object methods. We implemented our work in a new language called Veracity, implemented in Multicore OCaml and on top of Servois [10] and the libcuckoo concurrent hashtable. We provide the first benchmarks programs with commute blocks. We show that commute conditions can be synthesized even for nonlinear programs; we confirm the expectation that concurrency speedups can be seen as the computation increases; and we apply our work to a small in-memory filesystem and a crowdfund blockchain smart contract.

Concurrent objects form the foundation of many applications that exploit multicore architectures. Reasoning about the fine-grained complexities (interleavings, invariants, etc.) of those data structures, however, is notoriously difficult. Formal proof methodologies for arguing about the correctness-i.e., linearizability-of these data structures are still somewhat disconnected from the intuitive correctness arguments.Intuitions are often about a few canonical executions, possibly with few threads, whereas formal proofs would often use generic but complex arguments about arbitrary interleavings over unboundedly many threads. As a way to bring formal proofs closer to intuitive arguments, we introduce a new methodology for characterizing the interleavings of concurrent objects, based on their commutativity quotient. This quotient represents every interleaving up to reordering of commutative steps and, when chosen carefully, admits simple abstractions in the form of regular or context-free languages that enable simple proofs of linearizability. We demonstrate these facts on a large class of lock-free data structures and the infamously difficult Herlihy-Wing Queue. We automate the discovery of these execution quotients and show it can be automatically done for challenging data-structures such as Treiber's stack, Michael/Scott Queue, and a concurrent Set implemented as a linked list.

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