Abstract

In scalable concurrent architectures, the performance of a parallel algorithm depends on the resource management policies used. Such policies determine, for example, how data is partitioned and distributed and how processes are scheduled. In particular, the performance of a parallel algorithm obtained by using a particular policy can be affected by increasing the size of the architecture or the input. In order to support scalability, we are developing a methodology for modular specification of partitioning and distribution strategies (PDS's). As a consequence, a PDS may be changed without modifying the code specifying the logic of a parallel algorithm. We illustrate our methodology for parallel algorithms that use dynamic data structures. 1 Introduction In parallel computing, the set of operations and the partial order in which they may be carried out define an ideal algorithm [25]. We use Actors, a form of concurrent objects, to specify ideal algorithms. Actors naturally express an i...

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