Handling Parallelism in a Concurrency Model
This addresses the need for safe parallelism in concurrency models for developers, but it is incremental as it builds on existing models like SCOOP.
The paper tackled the problem of enabling data parallelism in concurrency models that restrict shared data access to prevent race errors, by presenting a library-based approach that allows parallel array access while maintaining safety; the result showed negligible performance overhead compared to threaded implementations in benchmark tests.
Programming models for concurrency are optimized for dealing with nondeterminism, for example to handle asynchronously arriving events. To shield the developer from data race errors effectively, such models may prevent shared access to data altogether. However, this restriction also makes them unsuitable for applications that require data parallelism. We present a library-based approach for permitting parallel access to arrays while preserving the safety guarantees of the original model. When applied to SCOOP, an object-oriented concurrency model, the approach exhibits a negligible performance overhead compared to ordinary threaded implementations of two parallel benchmark programs.