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Background: The huge quantity of data produced in Biomedical research needssophisticated algorithmic methodologies for its storage, analysis, andprocessing. High Performance Computing (HPC) appears as a magic bullet in thischallenge. However, several hard to solve parallelization and load balancingproblems arise in this context. Here we discuss the HPC-oriented implementationof a general purpose learning algorithm, originally conceived for DNA analysisand recently extended to treat uncertainty on data (U BRAIN). The U-BRAINalgorithm is a learning algorithm that finds a Boolean formula in disjunctivenormal form (DNF), of approximately minimum complexity, that is consistent witha set of data (instances) which may have missing bits. The conjunctive terms ofthe formula are computed in an iterative way by identifying, from the givendata, a family of sets of conditions that must be satisfied by all the positiveinstances and violated by all the negative ones; such conditions allow thecomputation of a set of coefficients (relevances) for each attribute (literal),that form a probability distribution, allowing the selection of the termliterals. The great versatility that characterizes it, makes U-BRAIN applicablein many of the fields in which there are data to be analyzed. However thememory 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.

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