Processing-in-memory for genomics workloads
Journal:
arXiv
Published Date:
May 31, 2025
Abstract
Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the main
workforce for 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 started the BioPIM Project to
leverage the 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 for the highest cost, energy, and time
savings benefit.
