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.

翻译：低成本、高通量的DNA与RNA测序（HTS）数据是生命科学研究的基石。基因组测序正逐渐成为预测性、预防性、个性化及参与性（统称为“P4”）医学的组成部分。目前，所有基因组数据均在能耗巨大的计算机集群和中心进行处理，这既需要数据传输，又消耗大量能源，并浪费宝贵时间。因此，亟需开发快速、高能效且低成本的技术，以实现在无需依赖数据中心和云平台的情况下开展基因组学研究。我们近期启动了BioPIM项目，旨在利用新兴的内存内处理（PIM）技术，实现对生物信息学工作负载的高能效与低成本分析。BioPIM项目聚焦于将基因组学中常用的算法和数据结构与多种PIM架构进行协同设计，以实现最大程度的成本、能源与时间节约。