Processing-in-memory for genomics workloads

TL;DR

This paper discusses leveraging processing-in-memory (PIM) technologies to improve the energy efficiency and speed of genomics data analysis, addressing current limitations of traditional data processing methods.

Contribution

The BioPIM Project introduces a co-design approach for algorithms and data structures tailored to PIM architectures for genomics workloads.

Findings

PIM can significantly reduce energy consumption in genomics analysis.

Co-designed algorithms optimize performance on PIM architectures.

Potential for cost and time savings in genomics research.

Abstract

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.