A Comparison of Task Parallel Frameworks based on Implicit Dependencies in Multi-core Environments

The larger flexibility that task parallelism offers with respect to data parallelism comes at the cost of a higher complexity due to the variety of tasks and the arbitrary patterns of dependences that they can exhibit. These dependencies should be expressed not only correctly, but optimally, i.e. avoiding over-constraints, in order to obtain the maximum performance from the underlying hardware. There have been many proposals to facilitate this non-trivial task, particularly within the scope of nowadays ubiquitous multi-core architectures. A very interesting family of solutions because of their large scope of application, ease of use and potential performance are those in which the user declares the dependences of each task, and lets the parallel programming framework figure out which are the concrete dependences that appear at runtime and schedule accordingly the parallel tasks. Nevertheless, as far as we know, there are no comparative studies of them that help users identify their relative advantages. In this paper we describe and evaluate four tools of this class discussing the strengths and weaknesses we have found in their use.