BC-GA: A Graph Partitioning Algorithm for Parallel Simulation of Internet Applications

A partitioning algorithm for parallel discrete event gate-level logic simulations is proposed in this paper. Unlike most other partitioning algorithms, the proposed algorithm preserves computation concurrency by assigning to processors circuit gates that can be evaluated at about the same time. As a result, the improved concurrency preserving partitioning (iCPP) algorithm can provide better load balancing throughout the period of a parallel simulation. This is especially important when the algorithm is used together with a Time Warp simulation where a high degree of concurrency can lead to fewer rollbacks and better performance. The algorithm consists of three phases and three conflicting goals can be separately considered so to reduce computational complexity.To evaluate the quality of partitioning algorithms in terms of preserving concurrency, a concurrency metric that requires neither sequential nor parallel simulation is proposed. A levelization technique is used in computing the metric so to determine gates which can be evaluated at about the same time. A parallel gate-level logic simulator is implemented on an INTEL Paragon and an IBM SP2 to evaluate the performance of the iCPP algorithm. The results are compared with several other partitioning algorithms to show that the iCPP algorithm does preserve concurrency pretty well and reasonable speedup may be achieved with the algorithm.

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Phylofactorization: a graph-partitioning algorithm to identify phylogenetic scales of ecological data

10.1101/235341 ◽

2017 ◽

Cited By ~ 4

Author(s):

Alex D. Washburne ◽

Justin D. Silverman ◽

James T. Morton ◽

Daniel J. Becker ◽

Daniel Crowley ◽

...

Keyword(s):

Body Mass ◽

Graph Partitioning ◽

R Package ◽

Ecological Groups ◽

Reduced Rank Regression ◽

Ecological Data ◽

Phylogenetic Constraint ◽

Partitioning Algorithm ◽

Relative Abundances ◽

Additive Modeling

AbstractThe problem of pattern and scale is a central challenge in ecology. The problem of scale is central to community ecology, where functional ecological groups are aggregated and treated as a unit underlying an ecological pattern, such as aggregation of “nitrogen fixing trees” into a total abundance of a trait underlying ecosystem physiology. With the emergence of massive community ecological datasets, from microbiomes to breeding bird surveys, there is a need to objectively identify the scales of organization pertaining to well-defined patterns in community ecological data.The phylogeny is a scaffold for identifying key phylogenetic scales associated with macroscopic patterns. Phylofactorization was developed to objectively identify phylogenetic scales underlying patterns in relative abundance data. However, many ecological data, such as presence-absences and counts, are not relative abundances, yet it is still desireable and informative to identify phylogenetic scales underlying a pattern of interest. Here, we generalize phylofactorization beyond relative abundances to a graph-partitioning algorithm for any community ecological data.Generalizing phylofactorization connects many tools from data analysis to phylogenetically-informe analysis of community ecological data. Two-sample tests identify three phylogenetic factors of mammalian body mass which arose during the K-Pg extinction event, consistent with other analyses of mammalian body mass evolution. Projection of data onto coordinates defined by the phylogeny yield a phylogenetic principal components analysis which refines our understanding of the major sources of variation in the human gut microbiome. These same coordinates allow generalized additive modeling of microbes in Central Park soils and confirm that a large clade of Acidobacteria thrive in neutral soils. Generalized linear and additive modeling of exponential family random variables can be performed by phylogenetically-constrained reduced-rank regression or stepwise factor contrasts. We finish with a discussion of how phylofac-torization produces an ecological species concept with a phylogenetic constraint. All of these tools can be implemented with a new R package available online.

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