Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology

Sadayappan, Ponnuswamy; Ltaief, Hatem; Worlikar, Umesh; Schultze, Hartmut; Chamberlain, Bradford L.; Juckeland, Guido; Singhal, Sharad; Ulas, Thomas; Agrawal, Shobhit; Becker, Matthias; Schultze, Joachim L.

doi:10.1007/978-3-030-50743-5_17

Contribution to a conference proceedings/Contribution to a book

DZNE-2022-01017

Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology

Sadayappan, P. (Editor) ; Chamberlain, B. L. (Editor) ; Juckeland, G. (Editor) ; Ltaief, H. (Editor) ; Becker, M. (First author)DZNE* ; Worlikar, U.DZNE* ; Agrawal, S.Extern* ; Schultze, H.Extern* ; Ulas, T.Extern* ; Singhal, S. ; Schultze, J. L. (Last author)DZNE*

2020
Springer International Publishing Cham
ISBN: 978-3-030-50742-8 (print), 978-3-030-50743-5 (electronic)

High Performance Computing / Sadayappan, Ponnuswamy (Editor) ; Cham : Springer International Publishing, 2020, Chapter 17 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-030-50742-8=978-3-030-50743-5 ; doi:10.1007/978-3-030-50743-5
International Conference on High Performance Computing, , Cham : Springer International Publishing, Lecture Notes in Computer Science 12151, 328 - 344 (2020) [10.1007/978-3-030-50743-5_17]

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Please use a persistent id in citations: doi:10.1007/978-3-030-50743-5_17

Abstract: Research is increasingly becoming data-driven, and natural sciences are not an exception. In both biology and medicine, we are observing an exponential growth of structured data collections from experiments and population studies, enabling us to gain novel insights that would otherwise not be possible. However, these growing data sets pose a challenge for existing compute infrastructures since data is outgrowing limits within compute. In this work, we present the application of a novel approach, Memory-Driven Computing (MDC), in the life sciences. MDC proposes a data-centric approach that has been designed for growing data sizes and provides a composable infrastructure for changing workloads. In particular, we show how a typical pipeline for genomics data processing can be accelerated, and application modifications required to exploit this novel architecture. Furthermore, we demonstrate how the isolated evaluation of individual tasks misses significant overheads of typical pipelines in genomics data processing.

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