Models and resource metrics for parallel and distributed computation
Various massively parallel computers have emerged in recent years. Each of then have some distinct architecture properties that challenge the computer scientist to develop algorithms and software appropriate to that specified architecture. This is at least as difficult a problem as software reuse in the sequential computer case. One approach to addressing this problem is to design parallel computation models which abstract the architecture details into several generic parameters, which we call resource metrics. Typical resource metrics include the number of processors, communication latency, synchronization, bandwidth, block transfer capability, memory access method, and network topology hierarchy. We review the various parallel and distributed computation models and compare the different resource metrics chosen by different computation models.
