In concurrent computing environments that are based on heterogeneous processing elements interconnected by general-purpose networks, several classes of overheads contribute to lowered performance. In an attempt to gain a deeper insight into the exact nature of these overheads, and to develop strategies to alleviate them, we have conducted empirical studies of selected applications representing different classes of concurrent programs. These analyses have identified load imbalance, the parallelism model adopted, communication delay and throughput, and system factors as the primary factors affecting performance in cluster environments. Based on the degree to which these factors affect specific classes of applications, we propose a combination of model selection criteria, partitioning strategies, and software system heuristics to reduce overheads and enhance performance in network based environments. We demonstrate that agenda parallelism and load balancing strategies contribu...

In an earlier research paper [HL1], we presented a novel, yet straightforward linear-time algorithm for merging two sorted lists in a fixed amount of additional space. Constant of proportionality estimates and empirical testing reveal that this procedure is reasonably competitive with merge routines free to squander unbounded additional memory, making it particularly attractive whenever space is a critical resource. In this paper, we devise a relatively simple strategy by which this efficient merge can be made stable, and extend our results in a nontrivial way to the problem of stable sorting by merging. We also derive upper bounds on our algorithms' constants of proportionality, suggesting that in some environments (most notably external file processing) their modest run-time premiums may be more than offset by the dramatic space savings achieved.

We present parallel algorithms for the elementary binary set operations that, given an EREW PRAM with k processors, operate on two sorted lists of total length n in O(n=k + log n) time and O(k) extra space, and are thus time-space optimal for any value of k n=(log n). Our methods are stable, require no information other than a record's key, and do not modify records as they execute. ii Symbols Used O capital Greek omicron of "big oh" notation 6 capital Greek sigma for summations [ set union " set intersection 8 set exclusive or iii 1. Introduction The design and analysis of optimal parallel file rearrangement algorithms has long been a topic of wide-spread attention. The vast majority of the published literature has concentrated on the search for algorithms that are time optimal , that is, those that achieve optimal speedup (see, for example, [AS]). Unfortunately, space management issues have often taken a back seat in these efforts, leaving those who seek to implement optima...