Minisymposium Presentation

Analyzing genomic data provides critical insights for understanding and treating diseases, outbreak tracing, evolutionary studies, agriculture, and many other areas of the life sciences and personalized medicine. Modern genome sequencing devices can rapidly generate large amounts of genomic data at a low cost. However, genome analysis is bottlenecked by the computational and data movement overheads of existing systems and algorithms, causing significant limitations in terms of speed, accuracy, application scope, and energy efficiency of the analysis. In this talk, we will focus on substantially improving the speed and energy efficiency of a computationally costly machine learning (ML) technique used in many important genomics applications. We will introduce ApHMM, which resolves significant inefficiencies that make an expectation-maximization technique costly for profile Hidden Markov Models (pHMMs) on general-purpose processors. ApHMM achieves this by effectively co-designing both hardware and algorithm. As a result, ApHMM provides substantial improvements in performance (up to two orders of magnitude) and energy efficiency (up to three orders of magnitude) compared to CPUs and GPUs.