GenCache: Leveraging In-Cache Operators for Efficient Sequence Alignment

Anirban Nag, C. N. Ramachandra, Rajeev Balasubramonian, Ryan Stutsman, Edouard Giacomin, Hari Kambalasubramanyam, Pierre-Emmanuel Gaillardon

Abstract: Precision Medicine will rely on frequent genomic analysis, especially for patients undergoing cancer treatments or suffering from rare diseases. Sequence alignment is invoked in multiple stages of the genomic analysis pipeline. Recent projects have introduced accelerators, GenAx and Darwin, for 2nd and 3rd generation sequencers respectively. In this work, we improve upon the GenAx design by increasing its parallelism and reducing its memory bandwidth demands. This is achieved with a combination of hardware and software innovations. We first integrate in-cache operators from prior work into the GenAx memory hierarchy; we then augment the in-cache peripheral circuit to support additional new operators. We then re-structure the sequence alignment algorithm to (i) leverage the many in-cache operators, (ii) exploit the common case in genomic datasets, (iii) use Bloom Filters to reduce futile accesses, and (iv) maximize data reuse within a re-organized memory hierarchy. While the baseline GenAx accelerator processes a batch of reads in 194 seconds while nearly saturating the 153.6 GB/s memory bandwidth, the proposed GenCache architecture processes the same batch of reads in 37 seconds at an improved energy efficiency of 8.6×, while demanding 20 GB/s average memory bandwidth. Our hardware and software techniques thus interact synergistically to target both memory and compute bottlenecks, while not affecting the outputs of the application. We show that the basic principles in GenCache can also be exploited by 3rd generation sequence aligners.