Integrating task parallelism in data parallel languages for parallel programming on NOWs

A number of high-level parallel programming platforms for networks of workstations (NOWs) have been developed in recent times. Most of these platforms target the exploitation of data parallelism in applications. They do not allow expressibility of applications as a collection of tasks along with their precedence relationships. As a result, the control or task parallelism in an application cannot be expressed or exploited. The current work aims at integrating the notion of task parallelism and precedence relationships among constituting tasks to such high-level data parallel platforms for NOWs. Our model of integration provides for arbitrary nesting of data and task parallel modules. Also, the precedence relationships are clearly reflected from the program structure. The model relieves the programmer from the need to design applications for non-determinism in the order of completion of constituting tasks. The design of the runtime support as well as system-level book keeping is discussed. The model is general enough to be applied to a wide range of data parallel platforms. A specific case of integrating the model into anonymous remote computing (ARC), a data parallel programming platform, is presented. The performance related aspects are also discussed. Copyright  2000 John Wiley & Sons, Ltd.