Computer Architecture-Aware Optimisation of DNA Analysis Systems

DNA sequencing---the process that converts chemically encoded data in DNA molecules into a computer-readable form---is revolutionising the field of medicine. DNA sequencers, the machines which perform DNA sequencing, have evolved from the size of a fridge to that of a mobile phone over the last two decades. The cost of sequencing a human genome also has reduced from billions of dollars to hundreds of dollars. Despite these improvements, DNA sequencers output hundreds or thousands of gigabytes of data that must be analysed on computers to discover meaningful information with biological implications. Unfortunately, the analysis techniques have not kept the pace with rapidly improving sequencing technologies. Consequently, even today, the process of DNA analysis is performed on high-performance computers, just as it was a couple of decades ago. Such high-performance computers are not portable. Consequently, the full utility of an ultra-portable sequencer for sequencing in-the-field or at the point-of-care is limited by the lack of portable lightweight analytic techniques. This thesis proposes computer architecture-aware optimisation of DNA analysis software. DNA analysis software is inevitably convoluted due to the complexity associated with biological data. Modern computer architectures are also complex. Performing architecture-aware optimisations requires the synergistic use of knowledge from both domains, (i.e, DNA sequence analysis and computer architecture). This thesis aims to draw the two domains together. In this thesis, gold-standard DNA sequence analysis workflows (a workflow is a few software tools executed sequentially where each software tool is a complex system of dozens of algorithms) are systematically examined for algorithmic components that cause performance bottlenecks. Identified bottlenecks are resolved through architecture-aware optimisations at different levels, i.e., memory, cache, register and processor. The optimised software tools are used in complete end-to-end analysis workflows and their efficacy is demonstrated by running on prototypical embedded systems. The embedded systems are not only fully functional, but the performance is also comparable to an unoptimised workflow on a high-performance computer. Such low cost, energy-efficient, sufficiently fast and portable embedded systems enable complete DNA analysis at the point-of-care or in-the-field