Graphics Processing Units : abstract modelling and applications in bioinformatics

Abstract

The Graphical Processing Unit is a specialised piece of hardware that contains many low powered cores, available on both the consumer and industrial market. The original Graphical Processing Units were designed for processing high quality graphical images, for presentation to the screen, and were therefore marketed to the computer games market segment. More recently, frameworks such as CUDA and OpenCL allowed the specialised highly parallel architecture of the Graphical Processing Unit to be used for not just graphical operations, but for general computation. This is known as General Purpose Programming on Graphical Processing Units, and it has attracted interest from the scientific community, looking for ways to exploit this highly parallel environment, which was cheaper and more accessible than the traditional High Performance Computing platforms, such as the supercomputer. This interest in developing algorithms that exploit the parallel architecture of the Graphical Processing Unit has highlighted the need for scientists to be able to analyse proposed algorithms, just as happens for proposed sequential algorithms. In this thesis, we study the abstract modelling of computation on the Graphical Processing Unit, and the application of Graphical Processing Unit-based algorithms in the field of bioinformatics, the field of using computational algorithms to solve biological problems. We show that existing abstract models for analysing parallel algorithms on the Graphical Processing Unit are not able to sufficiently and accurately model all that is required. We propose a new abstract model, called the Abstract Transferring Graphical Processing Unit Model, which is able to provide analysis of Graphical Processing Unit-based algorithms that is more accurate than existing abstract models. It does this by capturing the data transfer between the Central Processing Unit and the Graphical Processing Unit. We demonstrate the accuracy and applicability of our model with several computational problems, showing that our model provides greater accuracy than the existing models, verifying these claims using experiments. We also contribute novel Graphics Processing Unit-base solutions to two bioinformatics problems: DNA sequence alignment, and Protein spectral identification, demonstrating promising levels of improvement against the sequential Central Processing Unit experiments.