
Abstract Designing parallel, distributed computations is a significant barrier to the effective use of contemporary equipment. One aspect of the barrier is the difficulty of partitioning a serial solution into a set of communicating computational subsets (e.g., processes) that can be distributed over heterogeneous processors in a distributed hardware environment. The Parallel Distributed computation Graph Model (ParaDiGM) and the VISual Assistant (VISA) have been designed to assist with the partitioning problem. The formal model is composed of two components: a micro model focuses on the functionality of the computation, while a consistent macro model explicitly represents the partition and the communication mechanisms. ParaDiGM encourages the designer to address functionality and partitioning in different submodels, maintaining a mapping between elements in the two submodels. ParaDiGM is formal, but has an intuitive visual presentation; its use is supported by the VISusal Assistant (VISA), a tool for designing, animating, simulating, and prototyping distributed computations. This note informally describes ParaDiGM and VISA, then illustrates how they can be used to assist with the design of parallel, distributed computations.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
