Processing-in-memory for genomics workloads
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910186199842816 |
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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 |