Models and resource metrics for parallel and distributed computation
Presents a framework of using resource metrics to characterize the various models of parallel computation. Our framework reflects the approach of recent models to abstract architectural details into several generic parameters, which we call resource metrics. We examine the different resource metrics chosen by different parallel models, categorizing the models into four classes: The basic synchronous models, and three extensions of the basic models which more accurately reflect practical machines by incorporating the notions of asynchrony, communication cost and memory hierarchy. We then present a new parallel computation model, the LogP-HMM model, as an illustration of design principles based on the framework of resource metrics. The LogP-HMM model extends an existing parameterized network model (LogP) with a sequential hierarchical memory model (HMM) characterizing each processor. The result accurately captures both network communication costs and the effects of multilevel memory, such as local cache and I/O. We examine the potential utility of our model in the design of near-optimal sorting and FFT algorithms.
