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Autori principali: Simon, William Andrew, Yavits, Leonid, Koliogeorgi, Konstantina, Falevoz, Yann, Shibuya, Yoshihiro, Lavenier, Dominique, Boybat, Irem, Zambaku, Klea, Şahin, Berkan, Sadrosadati, Mohammad, Mutlu, Onur, Sebastian, Abu, Chikhi, Rayan, Consortium, The BioPIM, Alkan, Can
Natura: Preprint
Pubblicazione: 2025
Soggetti:
Accesso online:https://arxiv.org/abs/2506.00597
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author Simon, William Andrew
Yavits, Leonid
Koliogeorgi, Konstantina
Falevoz, Yann
Shibuya, Yoshihiro
Lavenier, Dominique
Boybat, Irem
Zambaku, Klea
Şahin, Berkan
Sadrosadati, Mohammad
Mutlu, Onur
Sebastian, Abu
Chikhi, Rayan
Consortium, The BioPIM
Alkan, Can
author_facet Simon, William Andrew
Yavits, Leonid
Koliogeorgi, Konstantina
Falevoz, Yann
Shibuya, Yoshihiro
Lavenier, Dominique
Boybat, Irem
Zambaku, Klea
Şahin, Berkan
Sadrosadati, Mohammad
Mutlu, Onur
Sebastian, Abu
Chikhi, Rayan
Consortium, The BioPIM
Alkan, Can
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2506_00597
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Processing-in-memory for genomics workloads
Simon, William Andrew
Yavits, Leonid
Koliogeorgi, Konstantina
Falevoz, Yann
Shibuya, Yoshihiro
Lavenier, Dominique
Boybat, Irem
Zambaku, Klea
Şahin, Berkan
Sadrosadati, Mohammad
Mutlu, Onur
Sebastian, Abu
Chikhi, Rayan
Consortium, The BioPIM
Alkan, Can
Genomics
Hardware Architecture
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.
title Processing-in-memory for genomics workloads
topic Genomics
Hardware Architecture
url https://arxiv.org/abs/2506.00597