Making threaded programs safe and easy to reason about is one of the chief difficulties in modern programming. This work provides an efficient execution model and implementation for SCOOP, a concurrency approach that provides not only data-race freedom but also pre/postcondition reasoning guarantees between threads. The extensions we propose influence the underlying semantics to increase the amount of concurrent execution that is possible, exclude certain classes of deadlocks, and enable greater performance.

Making threaded programs safe and easy to reason about is one of the chief difficulties in modern programming. This work provides an efficient execution model for SCOOP, a concurrency approach that provides not only data-race freedom but also pre/postcondition reasoning guarantees between threads. The extensions we propose influence both the underlying semantics to increase the amount of concurrent execution that is possible, exclude certain classes of deadlocks, and enable greater performance. These extensions are used as the basis of an efficient runtime and optimization pass that improve performance 15x over a baseline implementation. This new implementation of SCOOP is, on average, also 2x faster than other well-known safe concurrent languages. The measurements are based on both coordination-intensive and data-manipulation-intensive benchmarks designed to offer a mixture of workloads.

Developers face a wide choice of programming languages and libraries supporting multicore computing. Ever more diverse paradigms for expressing parallelism and synchronization become available while their influence on usability and performance remains largely unclear. This paper describes an experiment comparing four markedly different approaches to parallel programming: Chapel, Cilk, Go, and Threading Building Blocks (TBB). Each language is used to implement sequential and parallel versions of six benchmark programs. The implementations are then reviewed by notable experts in the language, thereby obtaining reference versions for each language and benchmark. The resulting pool of 96 implementations is used to compare the languages with respect to source code size, coding time, execution time, and speedup. The experiment uncovers strengths and weaknesses in all approaches, facilitating an informed selection of a language under a particular set of requirements. The expert review step furthermore highlights the importance of expert knowledge when using modern parallel programming approaches.

Concurrency is an integral part of many robotics applications, due to the need for handling inherently parallel tasks such as motion control and sensor monitoring. Writing programs for this complex domain can be hard, in particular because of the difficulties of retaining a robust modular design. We propose to use SCOOP, an object-oriented programming model for concurrency which by construction is free of data races, therefore excluding a major class of concurrent programming errors. Synchronization requirements are expressed by waiting on routine preconditions, which turns out to provide a natural framework for implementing coordination requirements in robotics applications. As demonstration application, we describe a control program for hexapod locomotion, whose implementation closely follows the corresponding behavioral specification given by the biological model. We compare the architecture with solutions expressed in more traditional approaches to robotic control applications.