Leveraging High Performance Computing for Bioinformatics: A Methodology that Enables a Reliable Decision-Making

Bioinformatics could greatly benefit from increased computational resources delivered by High Performance Computing. However, the decision-making about which is the best architecture to deliver good performance for a set of Bioinformatics applications is a hard task. The traditional way is finding the architecture with a high theoretical peak of performance, obtained with benchmark tests. But, this is not an assured way for this decision, because each application of Bioinformatics has different computational requirements, which frequently are much different from usual benchmarks. We developed a methodology that assists researchers, even when their specialty is not high performance computing, to define the best computational infrastructure focused on their set of scientific application requirements. For this purpose, the methodology enables to define representative evaluation tests, including a model to define the correct benchmark, when the tests endorsed by the methodology could not be fully used. Further, a Gain Function allows a reliable decision-making based on the performances of a set of applications and architectures. It is also possible to consider the relative importance between applications and also between cost and performance.