Accelerated Genomics Data Processing using MemoryDriven Computing 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 realworld applications requires to rethink our current compute infrastructures. Here we provide evidence that MemoryDriven Computing (MDC), a novel memorycentric hardware architecture, is an attractive alternative to current processorcentric compute infrastructures. To illustrate how MDC can change NGS data handling, we used RNAseq pseudoalignment followed by quantification as a first example. Even more impressive, pseudoalignment by nearoptimal probabilistic RNAseq quantification (kallisto) was accelerated by more than two orders of magnitude with identical accuracy and indicated 66% reduced energy consumption. One billion RNAseq reads were processed in just 92 seconds. Clearly, MDC simultaneously reduces data processing time and energy consumption. Together with the MDCinherent 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 highend sequencers with local MDC, thereby also reducing movement of large raw data to central cloud storage. We further envision that other datarich areas will similarly benefit from this new memorycentric compute architecture.

MemAlign: A Memory Structure to Accelerate Gene Sequencing Since 2003, when the human reference genome was discovered, several applications found gene sequencing a promising mechanism to help improve their results. These include studying hereditary diseases, prenatal monitoring and others. Gene sequencing in its typical configuration consists of several elaborate processing stages, each performed by a separate software package. The intermediate results are transferred via large files between gene sequencing different steps, hence making gene sequencing a processing and IO demanding task. Taking advantage of advances memory speed and capacity and with the ultimate goal of pipelining the gene sequencing steps and avoiding utilizing file storage to communicate intermediate results, in this paper we present MemAlign, a novel presorted memory structure to pipeline the first and second processing steps of gene sequencing; Alignment and Sort. The number of memory locations of MemAlign corresponds the positions on the human reference genome. Combined with techniques to compress the alignment results, MemAlign essentially eliminates the Sort step by storing alignment results in the memory location that corresponds to the alignment position. MemAlign not only speeds up the combined processing time of Alignment and Sort, but also saves the considerable amount of storage required to store the intermediate results between the two steps.