XPS: FULL: DSD: End-to-end Acceleration of Genomic Workflows on Emerging Heterogeneous Supercomputers

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The proposed research harnesses parallelism to accelerate thepervasive bioinformatics workflow of detecting genetic variations.This workflow determines the genetic variants present in anindividual, given DNA sequencing data. The variant detection workflowis an integral part of current genomic data analysis, and severalstudies have linked genetic variants to diseases. Typical instancesof this workflow currently take several hours to multiple days tocomplete with state-of-the-art software, and current algorithms andsoftware are unable to exploit and benefit from even modest levels ofhardware parallelism. Most prior approaches to parallelization andperformance tuning of genomic data analysis pipelines have targetedcomputation, I/O, or network data transfer bottlenecks in isolation,and consequently, are limited in the overall performance improvementthey can achieve. This project targets end-to-end accelerationmethodologies and uses emerging heterogeneous supercomputers toreduce workflow time-to-completion.The project focuses on holistic methodologies to accelerate multiplecomponents within the genetic variant detection workflow. It exploreslightweight data reorganizations at multiple granularities to enhancelocality, investigates compute-, communication-, and I/O taskcotuning, locality-aware load-balancing, and coordinated resourcepartitioning to exploit high-performance computing platforms. A keygoal of the proposed research is to design domain-specificoptimizations targeting the massive parallelism and scalabilitypotential of current heterogeneous supercomputers, so that thedeveloped techniques can be easily transferred and applied to dedicated academic cluster and commercial computational environments.Outreach efforts target undergraduate students through recruitingworkshops and attract them to interdisciplinary graduate programs.Curriculum development activities emphasize cross-layer parallelism.For further information, see project web site at http://sites.psu.edu/XPSGenomics