Memory-driven computing accelerates genomic data processing

Becker, M.; Chabbi, M.; Warnat-Herresthal, S.; Klee, K.; Schulte-Schrepping, J.; Biernat, P.; Guenther, P.; Bassler, K.; Craig, R.; Schultze, H.; Singhal, S.; Ulas, T.; Schultze, J. L.

doi:10.1101/519579

bioRxiv · 10.1101/519579

Memory-driven computing accelerates genomic data processing

Becker, M.·Chabbi, M.·Warnat-Herresthal, S.·Klee, K.·Schulte-Schrepping, J.·Biernat, P.·Guenther, P.·Bassler, K.·Craig, R.·Schultze, H.·Singhal, S.·Ulas, T.·Schultze, J. L.

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Abstract

Next generation sequencing (NGS) is the driving force behind precision medicine and is revolutionizing most, if not all, areas of the life sciences. Particularly when targeting the major common diseases, an exponential growth of NGS data is foreseen for the next decades. This enormous increase of NGS data and the need to process the data quickly for real-world applications requires to rethink our current compute infrastructures. Here we provide evidence that memory-driven computing (MDC), a novel memory-centric hardware architecture, is an attractive alternative to current processor-centric compute infrastructures. To illustrate how MDC can change NGS data handling, we used RNA-seq assembly and pseudoalignment followed by quantification as two first examples. Adapting transcriptome assembly pipelines for MDC reduced compute time by 5.9-fold for the first step (SAMtools). Even more impressive, pseudoalignment by near-optimal probabilistic RNA-seq quantification (kallisto) was accelerated by more than two orders of magnitude with identical accuracy and indicated 66% reduced energy consumption. One billion RNA-seq reads were processed in just 92 seconds. Clearly, MDC simultaneously reduces data processing time and energy consumption. Together with the MDC-inherent solutions for local data privacy, a new compute model can be projected pushing large scale NGS data processing and primary data analytics closer to the edge by directly combining high-end sequencers with local MDC, thereby also reducing movement of large raw data to central cloud storage. We further envision that other data-rich areas will similarly benefit from this new memory-centric compute architecture.

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Becker, M., Chabbi, M., Warnat-Herresthal, S., Klee, K., Schulte-Schrepping, J., Biernat, P., Guenther, P., Bassler, K., Craig, R., Schultze, H., Singhal, S., Ulas, T., Schultze, J. L.. 2019-01-13. Memory-driven computing accelerates genomic data processing. https://doi.org/10.1101/519579

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