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

TL;DR

This paper discusses a high-performance computing implementation of the U-BRAIN learning algorithm, designed for bioinformatics data analysis, addressing challenges in parallelization and load balancing to handle large datasets efficiently.

Contribution

It presents a parallel HPC-oriented implementation of U-BRAIN, optimizing its computational performance for large-scale bioinformatics applications.

Findings

Achieved significant speedup in execution time.

Reduced memory usage through parallelization.

Demonstrated scalability on large datasets.

Abstract

Background: The 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.