Optimizing Pattern-Matching for Memory-Efficient Heterogeneous DNA Processing in Bioinformatics.

We are proposing a new, memory-efficient approach to optimizing DNA pattern-matching in bioinformatics through a heterogeneous implementation and new architectural layout, that poses several advantages over usual approaches, which we discuss in detail. We are applying our approach on a subset of DNA sequences part of the FASTA open database, under different hardware settings, and observe a significant performance increase in our heterogeneous implementation. With a practical reduction of 23 times less memory usage than a classic implementation of the same algorithm, and massive scaling capabilities for high-throughput DNA-matching, our approach proves its feasibility for scalable heterogeneous architectures.