Processing-in-memory for genomics workloads
This work addresses the need for efficient genomic data analysis for life sciences and personalized medicine, but the abstract lacks concrete results or numbers, making it an early-stage proposal.
The BioPIM Project aims to leverage processing-in-memory (PIM) technologies to enable fast, energy-efficient, and cost-efficient analysis of genomics workloads, addressing the high energy and time costs of current data center-based approaches.
Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the backbone of the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized, and Participatory (termed 'P4') medicine. All genomic data are currently processed in energy-hungry computer clusters and centers, necessitating data transfer, consuming substantial energy, and wasting valuable time. Therefore, there is a need for fast, energy-efficient, and cost-efficient technologies that enable genomics research without requiring data centers and cloud platforms. We recently launched the BioPIM Project to leverage emerging processing-in-memory (PIM) technologies to enable energy- and cost-efficient analysis of bioinformatics workloads. The BioPIM Project focuses on co-designing algorithms and data structures commonly used in genomics with several PIM architectures to achieve the highest cost, energy, and time savings.