Principles for problem aggregation and assignment in medium scale multiprocessors

One of the most important issues in parallel processing is the mapping of workload to processors. This paper considers a large class of problems having a high degree of potential fine grained parallelism, and execution requirements that are either not predictable, or are too costly to predict. The main issues in mapping such a problem onto medium scale multiprocessors are those of aggregation and assignment. We study a method of parameterized aggregation that makes few assumptions about the workload. The mapping of aggregate units of work onto processors is uniform, and exploits locality of workload intensity to balance the unknown workload. In general, a finer aggregate granularity leads to a better balance at the price of increased communication/synchronization costs; the aggregation parameters can be adjusted to find a reasonable granularity. The effectiveness of this scheme is demonstrated on three model problems: an adaptive one-dimensional fluid dynamics problem with message passing, a sparse triangular linear system solver on both a shared memory and a message-passing machine, and a two-dimensional time-driven battlefield simulation employing message passing. Using the model problems, the tradeoffs are studied between balanced workload and the communication/synchronization costs. Finally, an analytical model is used to explain why the method balances workload and minimizes the variance in system behavior

Optimal pre-scheduling of problem remappings

A large class of scientific computational problems can be characterized as a sequence of steps where a significant amount of computation occurs each step, but the work performed at each step is not necessarily identical. Two good examples of this type of computation are: (1) regridding methods which change the problem discretization during the course of the computation, and (2) methods for solving sparse triangular systems of linear equations. Recent work has investigated a means of mapping such computations onto parallel processors; the method defines a family of static mappings with differing degrees of importance placed on the conflicting goals of good load balance and low communication/synchronization overhead. The performance tradeoffs are controllable by adjusting the parameters of the mapping method. To achieve good performance it may be necessary to dynamically change these parameters at run-time, but such changes can impose additional costs. If the computation's behavior can be determined prior to its execution, it can be possible to construct an optimal parameter schedule using a low-order-polynomial-time dynamic programming algorithm. Since the latter can be expensive, the performance is studied of the effect of a linear-time scheduling heuristic on one of the model problems, and it is shown to be effective and nearly optimal

An analysis of scatter decomposition

A formal analysis of a powerful mapping technique known as scatter decomposition is presented. Scatter decomposition divides an irregular computational domain into a large number of equal sized pieces, and distributes them modularly among processors. A probabilistic model of workload in one dimension is used to formally explain why, and when scatter decomposition works. The first result is that if correlation in workload is a convex function of distance, then scattering a more finely decomposed domain yields a lower average processor workload variance. The second result shows that if the workload process is stationary Gaussian and the correlation function decreases linearly in distance until becoming zero and then remains zero, scattering a more finely decomposed domain yields a lower expected maximum processor workload. Finally it is shown that if the correlation function decreases linearly across the entire domain, then among all mappings that assign an equal number of domain pieces to each processor, scatter decomposition minimizes the average processor workload variance. The dependence of these results on the assumption of decreasing correlation is illustrated with situations where a coarser granularity actually achieves better load balance

Pond characteristics as determinants of species diversity and community composition in desert bats

Although water availability is known to affect landscape-scale patterns of wildlife diversity and distribution in arid environments, little is known about the microhabitat characteristics that shape the local-scale distribution of desert bats. We examined the relative importance of pond microhabitat characteristics for the conservation of bats, and hypothesized that in arid environments, patterns of bat diversity and community composition relate to the size of the pond and its hydroperiod (the number of months a pond holds water), a term we use to distinguish between permanent, semi-permanent and temporary ponds. We combined acoustic monitoring with video recording and an experimental approach to study bat activity over natural ponds in the Negev Desert, Israel. We found that both within and between ponds bat species richness and activity significantly increased with pond size. An experimental reduction of pond size led to a significant reduction in bat species richness and activity and affected the bat community composition. In contrast to pond size, pond hydroperiod did not affect bat diversity, as temporary ponds had equivalent levels of bat species richness and activity to permanent ponds. However, hydroperiod did couple with pond size to affect the bat community composition, whereby non-desert bat species that have a higher frequency of drinking were associated with larger and more permanent ponds. Our results highlight the importance of larger temporary ponds (ponds over 15 m in length and 0.5 m in depth) for the conservation of biodiversity in arid environments

Does interspecific competition drive patterns of habitat use in desert bat communities?

Bodies of water are a key foraging habitat for insectivorous bats. Since water is a scarce and limiting resource in arid environments, bodies of open water may have a structuring effect on desert bat communities, resulting in temporal or spatial partitioning of bat activity. Using acoustic monitoring, we studied the spatial and temporal activity patterns of insectivorous bats over desert ponds, and hypothesised that sympatric bat species partition the foraging space above ponds based on interspecific competitive interactions. We used indirect measures of competition (niche overlap and competition coefficients from the regression method) and tested for differences in pond habitat selection and peak activity time over ponds. We examined the effect of changes in the activity of bat species on their potential competitors. We found that interspecific competition affects bat community structure and activity patterns. Competing species partitioned their use of ponds spatially, whereby each species was associated with different pond size and hydroperiod (the number of months a pond holds water) categories, as well as temporally, whereby their activity peaked at different hours of the night. The drying out of temporary ponds increased temporal partitioning over permanent ponds. Differences in the activity of species over ponds in response to the presence or absence of their competitors lend further support to the role of interspecific competition in structuring desert bat communities. We suggest that habitat use and night activity pattern of insectivorous bats in arid environments reflect the trade-offs between selection of preferred pond type or activity time and constraints posed by competitive interactions

Distributed Community Detection with the WCC Metric

Community detection has become an extremely active area of research in recent years, with researchers proposing various new metrics and algorithms to address the problem. Recently, the Weighted Community Clustering (WCC) metric was proposed as a novel way to judge the quality of a community partitioning based on the distribution of triangles in the graph, and was demonstrated to yield superior results over other commonly used metrics like modularity. The same authors later presented a parallel algorithm for optimizing WCC on large graphs. In this paper, we propose a new distributed, vertex-centric algorithm for community detection using the WCC metric. Results are presented that demonstrate the algorithm's performance and scalability on up to 32 worker machines and real graphs of up to 1.8 billion vertices. The algorithm scales best with the largest graphs, and to our knowledge, it is the first distributed algorithm for optimizing the WCC metric.Comment: 6 pages, 6 figure

Social Networks in Wild Asses: Comparing Patterns and Processes among Populations

Asiatic wild asses inhabit some of the most arid environments in the world. All live in fissionfusion societies, but demography varies and the deserts in which they live often differ in subtle ways. Characterizing details of social structure of wild ass populations has been a challenge and has made it difficult to determine causes and consequences of any differences that might exist. We use network theory to compare the social structures of two populations of Asiatic asses/ onagers inhabiting the Negev desert, Israel and khur of the Little Rann of Kuch, India and show that populations differ in important structural ways that represent adaptive responses to variations in ecological demographic and phenotypic circumstances. Our analyses show that onagers inhabiting more variable environments then khur also live in larger, more cohesive groups than khur. Presumably networks with this structure facilitate the spread of information and foster cooperation. We also show that demography matters since social fragmentation increases as populations grow. Increases in the number of components in populations, reductions in the number of associates and diminished cliquishness within components, appear to be adaptive responses to integrating increasing numbers of individuals into social networks. We also find some support for the idea that social connectedness varies with phenotype. In our larger populations, non-lactating females who are most challenged in finding sparse feeding sites, are more selective than lactating females in their choice of strong associates. Presumably networks with this structure enhance foraging success by increasing information flow among like-minded individuals. As our study demonstrates, network analysis facilitates testing predictions about the cause of social structure and its impact on transmission processes