This paper presents a randomized scheduler for finding concurrency bugs. Like current stress-testing methods, it repeatedly runs a given test program with supplied inputs. However, it improves on stress-testing by finding buggy schedules more effectively and by quantifying the probability of missing concurrency bugs. Key to its design is the characterization of the depth of a concurrency bug as the minimum number of scheduling constraints required to find it. In a single run of a program with n threads and k steps, our scheduler detects a concurrency bug of depth d with probability at least 1/nk d−1 . We hypothesize that in practice, many concurrency bugs (including well-known types such as ordering errors, atomicity violations, and deadlocks) have small bug-depths, and we confirm the efficiency of our schedule randomization by detecting previously unknown and known concurrency bugs in several production-scale concurrent programs.

Building applications that are responsive and can exploit parallel hardware while remaining simple to write, understand, test, and maintain, poses an important challenge for developers. In particular, it is often desirable to enable various tasks to read or modify shared data concurrently without requiring complicated locking schemes that may throttle concurrency and introduce bugs.We introduce a mechanism that simplifies the parallel execution of different application tasks. Programmers declare what data they wish to share between tasks by using isolation types, and execute tasks concurrently by forking and joining revisions. These revisions are isolated: they read and modify their own private copy of the shared data only. A runtime creates and merges copies automatically, and resolves conflicts deterministically, in a manner declared by the chosen isolation type.To demonstrate the practical viability of our approach, we developed an efficient algorithm and an implementation in the form of a C# library, and used it to parallelize an interactive game application. Our results show that the parallelized game, while simple and very similar to the original sequential game, achieves satisfactory speedups on a multicore processor.

Geographically distributed systems often rely on replicated eventually consistent data stores to achieve availability and performance. To resolve conflicting updates at different replicas, researchers and practitioners have proposed specialized consistency protocols, called replicated data types, that implement objects such as registers, counters, sets or lists. Reasoning about replicated data types has however not been on par with comparable work on abstract data types and concurrent data types, lacking specifications, correctness proofs, and optimality results. To fill in this gap, we propose a framework for specifying replicated data types using relations over events and verifying their implementations using replication-aware simulations. We apply it to 7 existing implementations of 4 data types with nontrivial conflict-resolution strategies and optimizations (last-writer-wins register, counter, multi-value register and observed-remove set). We also present a novel technique for obtaining lower bounds on the worst-case space overhead of data type implementations and use it to prove optimality of 4 implementations. Finally, we show how to specify consistency of replicated stores with multiple objects axiomatically, in analogy to prior work on weak memory models. Overall, our work provides foundational reasoning tools to support research on replicated eventually consistent stores.

Parallel or incremental versions of an algorithm can significantly outperform their counterparts, but are often difficult to develop. Programming models that provide appropriate abstractions to decompose data and tasks can simplify parallelization. We show in this work that the same abstractions can enable both parallel and incremental execution.We present a novel algorithm for parallel self-adjusting computation. This algorithm extends a deterministic parallel programming model (concurrent revisions) with support for recording and repeating computations. On record, we construct a dynamic dependence graph of the parallel computation. On repeat, we reexecute only parts whose dependencies have changed.We implement and evaluate our idea by studying five example programs, including a realistic multi-pass CSS layout algorithm. We describe programming techniques that proved particularly useful to improve the performance of self-adjustment in practice. Our final results show significant speedups on all examples (up to 37x on an 8-core machine). These speedups are well beyond what can be achieved by parallelization alone, while requiring a comparable effort by the programmer.

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