VLSI implementation of greedy-based distributed routing schemes for ad hoc networks

Aloisio, A.; Izzo, V.; Rampone, Salvatore

doi:10.1007/s00500-006-0138-7

Cited by 5 publications

(7 citation statements)

References 26 publications

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“…Here we formalize such a cirumstance by representing G as a subset of 0, 1 2 , 1 n , where 1 2 represents the uncertain values.…”

Section: Adding Uncertaintymentioning

confidence: 99%

“…In what follows, by an instance we shall mean an element of 0, 1 2 , 1 n and, if it is in {0, 1} n it will be called certain.…”

Section: Adding Uncertaintymentioning

confidence: 99%

“…, v q . In this case, the problem is equivalent to a set covering one and we follow the BRAIN algorithm or, in a different version, the set covering approximation improvement presented in [1]. Recalling that, for a fuzzy subset F of a given set S, the fuzzy cardinality is defined as…”

Section: One-to-manymentioning

confidence: 99%

“…In order to deal with training data with incompletely observed attributes, we generalize the machine learning algorithm BRAIN (Batch Relevancebased Artificial INtelligence) [16] for binary classification rules. This algorithm was originally conceived for recognizing splice junctions in human DNA (see also [17,1]). Splice junctions are points on a DNA sequence at which "superfluous" DNA is removed during the process of protein synthesis in higher organisms [7].…”

Section: Introductionmentioning

confidence: 99%

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A fuzzified BRAIN algorithm for learning DNF from incomplete data

Rampone,

Russo

2010

Preprint

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Aim of this paper is to address the problem of learning Boolean functions from training data with missing values. We present an extension of the BRAIN algorithm, called U-BRAIN (Uncertainty-managing Batch Relevance-based Artificial INtelligence), conceived for learning DNF Boolean formulas from partial truth tables, possibly with uncertain values or missing bits.Such an algorithm is obtained from BRAIN by introducing fuzzy sets in order to manage uncertainty. In the case where no missing bits are present, the algorithm reduces to the original BRAIN.

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“…Here we formalize such a cirumstance by representing G as a subset of 0, 1 2 , 1 n , where 1 2 represents the uncertain values.…”

Section: Adding Uncertaintymentioning

confidence: 99%

“…In what follows, by an instance we shall mean an element of 0, 1 2 , 1 n and, if it is in {0, 1} n it will be called certain.…”

Section: Adding Uncertaintymentioning

confidence: 99%

Section: One-to-manymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fuzzified BRAIN algorithm for learning DNF from incomplete data

Rampone,

Russo

2010

Preprint

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“…Recently, in order to deal with incomplete training data, a machine learning algorithm, BRAIN (Batch Relevance-based Artificial INtelligence) [ 30 ], for binary classification rules has been generalized (U-BRAIN) [ 31 ]. This algorithm was originally conceived for recognizing splice junctions in human DNA (see also [ 32 , 33 ]). Splice junctions are points on a DNA sequence at which "superfluous" DNA is removed during the process of protein synthesis in higher organisms [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications

D’Angelo

Rampone

2014

BMC Bioinformatics

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BackgroundThe huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U-BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n3) and of O(n5) order, respectively, and so, the algorithm is unaffordable for huge data sets.ResultsWe find mathematical and programming solutions able to lead us towards the implementation of the algorithm U-BRAIN on parallel computers. First we give a Dynamic Programming model of the U-BRAIN algorithm, then we minimize the representation of the relevances. When the data are of great size we are forced to use the mass memory, and depending on where the data are actually stored, the access times can be quite different. According to the evaluation of algorithmic efficiency based on the Disk Model, in order to reduce the costs of the communications between different memories (RAM, Cache, Mass, Virtual) and to achieve efficient I/O performance, we design a mass storage structure able to access its data with a high degree of temporal and spatial locality. Then we develop a parallel implementation of the algorithm. We model it as a SPMD system together to a Message-Passing Programming Paradigm. Here, we adopt the high-level message-passing systems MPI (Message Passing Interface) in the version for the Java programming language, MPJ. The parallel processing is organized into four stages: partitioning, communication, agglomeration and mapping. The decomposition of the U-BRAIN algorithm determines the necessity of a communication protocol design among the processors involved. Efficient synchronization design is also discussed.ConclusionsIn the context of a collaboration between public and private institutions, the parallel model of U-BRAIN has been implemented and tested on the INTEL XEON E7xxx and E5xxx family of the CRESCO structure of Italian National Agency for New Tec...

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