11,911 research outputs found
Parallel symbolic state-space exploration is difficult, but what is the alternative?
State-space exploration is an essential step in many modeling and analysis
problems. Its goal is to find the states reachable from the initial state of a
discrete-state model described. The state space can used to answer important
questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a
starting point for sophisticated investigations expressed in temporal logic.
Unfortunately, the state space is often so large that ordinary explicit data
structures and sequential algorithms cannot cope, prompting the exploration of
(1) parallel approaches using multiple processors, from simple workstation
networks to shared-memory supercomputers, to satisfy large memory and runtime
requirements and (2) symbolic approaches using decision diagrams to encode the
large structured sets and relations manipulated during state-space generation.
Both approaches have merits and limitations. Parallel explicit state-space
generation is challenging, but almost linear speedup can be achieved; however,
the analysis is ultimately limited by the memory and processors available.
Symbolic methods are a heuristic that can efficiently encode many, but not all,
functions over a structured and exponentially large domain; here the pitfalls
are subtler: their performance varies widely depending on the class of decision
diagram chosen, the state variable order, and obscure algorithmic parameters.
As symbolic approaches are often much more efficient than explicit ones for
many practical models, we argue for the need to parallelize symbolic
state-space generation algorithms, so that we can realize the advantage of both
approaches. This is a challenging endeavor, as the most efficient symbolic
algorithm, Saturation, is inherently sequential. We conclude by discussing
challenges, efforts, and promising directions toward this goal
Dependability in Aggregation by Averaging
Aggregation is an important building block of modern distributed
applications, allowing the determination of meaningful properties (e.g. network
size, total storage capacity, average load, majorities, etc.) that are used to
direct the execution of the system. However, the majority of the existing
aggregation algorithms exhibit relevant dependability issues, when prospecting
their use in real application environments. In this paper, we reveal some
dependability issues of aggregation algorithms based on iterative averaging
techniques, giving some directions to solve them. This class of algorithms is
considered robust (when compared to common tree-based approaches), being
independent from the used routing topology and providing an aggregation result
at all nodes. However, their robustness is strongly challenged and their
correctness often compromised, when changing the assumptions of their working
environment to more realistic ones. The correctness of this class of algorithms
relies on the maintenance of a fundamental invariant, commonly designated as
"mass conservation". We will argue that this main invariant is often broken in
practical settings, and that additional mechanisms and modifications are
required to maintain it, incurring in some degradation of the algorithms
performance. In particular, we discuss the behavior of three representative
algorithms Push-Sum Protocol, Push-Pull Gossip protocol and Distributed Random
Grouping under asynchronous and faulty (with message loss and node crashes)
environments. More specifically, we propose and evaluate two new versions of
the Push-Pull Gossip protocol, which solve its message interleaving problem
(evidenced even in a synchronous operation mode).Comment: 14 pages. Presented in Inforum 200
Running real time distributed simulations under Linux and CERTI
This paper presents some experiments and some results to enforce real time distributed simulations in accordance with the High Level Architecture (HLA). Simulations were run by using CERTI, an open source middleware, as the Run Time Infrastructure (RTI). Models were distributed over computers under various available versions of the 2.6 Linux kernel. Studies and experiments relied on a real case study. The chosen case study was the simulation of an "in formation" flight of observation satellites. This case study brings up some real applicative needs in real time distributed simulations and real configurations of simulators and models. Two simulations of "in formation" flight of satellites were studied. The study consisted in modeling the behaviour of the simulators and in running these models by using various kernel or middleware operating mechanisms and services. Time measurements were performed at each test giving some results on the ability of the simulation to meet its real time requirements
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
Asynchronous iterative computations with Web information retrieval structures: The PageRank case
There are several ideas being used today for Web information retrieval, and
specifically in Web search engines. The PageRank algorithm is one of those that
introduce a content-neutral ranking function over Web pages. This ranking is
applied to the set of pages returned by the Google search engine in response to
posting a search query. PageRank is based in part on two simple common sense
concepts: (i)A page is important if many important pages include links to it.
(ii)A page containing many links has reduced impact on the importance of the
pages it links to. In this paper we focus on asynchronous iterative schemes to
compute PageRank over large sets of Web pages. The elimination of the
synchronizing phases is expected to be advantageous on heterogeneous platforms.
The motivation for a possible move to such large scale distributed platforms
lies in the size of matrices representing Web structure. In orders of
magnitude: pages with nonzero elements and bytes
just to store a small percentage of the Web (the already crawled); distributed
memory machines are necessary for such computations. The present research is
part of our general objective, to explore the potential of asynchronous
computational models as an underlying framework for very large scale
computations over the Grid. The area of ``internet algorithmics'' appears to
offer many occasions for computations of unprecedent dimensionality that would
be good candidates for this framework.Comment: 8 pages to appear at ParCo2005 Conference Proceeding
A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization
Based on the idea of randomized coordinate descent of -averaged
operators, a randomized primal-dual optimization algorithm is introduced, where
a random subset of coordinates is updated at each iteration. The algorithm
builds upon a variant of a recent (deterministic) algorithm proposed by V\~u
and Condat that includes the well known ADMM as a particular case. The obtained
algorithm is used to solve asynchronously a distributed optimization problem. A
network of agents, each having a separate cost function containing a
differentiable term, seek to find a consensus on the minimum of the aggregate
objective. The method yields an algorithm where at each iteration, a random
subset of agents wake up, update their local estimates, exchange some data with
their neighbors, and go idle. Numerical results demonstrate the attractive
performance of the method. The general approach can be naturally adapted to
other situations where coordinate descent convex optimization algorithms are
used with a random choice of the coordinates.Comment: 10 page
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