37,548 research outputs found
Networks of Evolutionary Processors (NEP) as Decision Support Systems
This paper presents the application of Networks of Evolutionary Processors to Decision Support
Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of
Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network.
The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time.
A working NEP example is shown
Networks of Evolutionary Processors: Java Implementation of a Threaded Processor
This paper is focused on a parallel JAVA implementation of a processor defined in a Network of
Evolutionary Processors. Processor description is based on JDom, which provides a complete, Java-based
solution for accessing, manipulating, and outputting XML data from Java code. Communication among different
processor to obtain a fully functional simulation of a Network of Evolutionary Processors will be treated in future.
A safe-thread model of processors performs all parallel operations such as rules and filters. A non-deterministic
behavior of processors is achieved with a thread for each rule and for each filter (input and output). Different
results of a processor evolution are shown
Doing-it-All with Bounded Work and Communication
We consider the Do-All problem, where cooperating processors need to
complete similar and independent tasks in an adversarial setting. Here we
deal with a synchronous message passing system with processors that are subject
to crash failures. Efficiency of algorithms in this setting is measured in
terms of work complexity (also known as total available processor steps) and
communication complexity (total number of point-to-point messages). When work
and communication are considered to be comparable resources, then the overall
efficiency is meaningfully expressed in terms of effort defined as work +
communication. We develop and analyze a constructive algorithm that has work
and a nonconstructive
algorithm that has work . The latter result is close to the
lower bound on work. The effort of each of
these algorithms is proportional to its work when the number of crashes is
bounded above by , for some positive constant . We also present a
nonconstructive algorithm that has effort
Online Permutation Routing in Partitioned Optical Passive Star Networks
This paper establishes the state of the art in both deterministic and
randomized online permutation routing in the POPS network. Indeed, we show that
any permutation can be routed online on a POPS network either with
deterministic slots, or, with high probability, with
randomized slots, where constant
. When , that we claim to be the
"interesting" case, the randomized algorithm is exponentially faster than any
other algorithm in the literature, both deterministic and randomized ones. This
is true in practice as well. Indeed, experiments show that it outperforms its
rivals even starting from as small a network as a POPS(2,2), and the gap grows
exponentially with the size of the network. We can also show that, under proper
hypothesis, no deterministic algorithm can asymptotically match its
performance
Space-Efficient Parallel Algorithms for Combinatorial Search Problems
We present space-efficient parallel strategies for two fundamental
combinatorial search problems, namely, backtrack search and branch-and-bound,
both involving the visit of an -node tree of height under the assumption
that a node can be accessed only through its father or its children. For both
problems we propose efficient algorithms that run on a -processor
distributed-memory machine. For backtrack search, we give a deterministic
algorithm running in time, and a Las Vegas algorithm requiring
optimal time, with high probability. Building on the backtrack
search algorithm, we also derive a Las Vegas algorithm for branch-and-bound
which runs in time, with high probability. A
remarkable feature of our algorithms is the use of only constant space per
processor, which constitutes a significant improvement upon previous algorithms
whose space requirements per processor depend on the (possibly huge) tree to be
explored.Comment: Extended version of the paper in the Proc. of 38th International
Symposium on Mathematical Foundations of Computer Science (MFCS
Replica determinism and flexible scheduling in hard real-time dependable systems
Fault-tolerant real-time systems are typically based on active replication where replicated entities are required to deliver their outputs in an identical order within a given time interval. Distributed scheduling of replicated tasks, however, violates this requirement if on-line scheduling, preemptive scheduling, or scheduling of dissimilar replicated task sets is employed. This problem of inconsistent task outputs has been solved previously by coordinating the decisions of the local schedulers such that replicated tasks are executed in an identical order. Global coordination results either in an extremely high communication effort to agree on each schedule decision or in an overly restrictive execution model where on-line scheduling, arbitrary preemptions, and nonidentically replicated task sets are not allowed. To overcome these restrictions, a new method, called timed messages, is introduced. Timed messages guarantee deterministic operation by presenting consistent message versions to the replicated tasks. This approach is based on simulated common knowledge and a sparse time base. Timed messages are very effective since they neither require communication between the local scheduler nor do they restrict usage of on-line flexible scheduling, preemptions and nonidentically replicated task sets
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