Leveraging Computational Storage for Power-Efficient Distributed Data Analytics

This article presents a family of computational storage drives (CSDs) and demonstrates their performance and power improvements due to in-storage processing (ISP) when running big data analytics applications. CSDs are an emerging class of solid state drives that are capable of running user code whil...

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Bibliographic Details
Published in:ACM transactions on embedded computing systems 2022-10, Vol.21 (6), p.1-36, Article 82
Main Authors: HeydariGorji, Ali, Rezaei, Siavash, Torabzadehkashi, Mahdi, Bobarshad, Hossein, Alves, Vladimir, Chou, Pai H.
Format: Article
Language:English
Subjects:
Computer systems organization
Computing methodologies
External storage
Hardware
Peer-to-peer architectures
Self-organization
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Summary:This article presents a family of computational storage drives (CSDs) and demonstrates their performance and power improvements due to in-storage processing (ISP) when running big data analytics applications. CSDs are an emerging class of solid state drives that are capable of running user code while minimizing data transfer time and energy. Applications that can benefit from in situ processing include distributed training, distributed inferencing, and databases. To achieve the full advantage of the proposed ISP architecture, we propose software solutions for workload balancing before and at runtime for training and inferencing applications. Other applications such as sharding-based databases can readily take advantage of our ISP structure without additional tooling. Experimental results on different capacity and form factors of CSDs show up to 3.1× speedup in processing while reducing the energy consumption and data transfer by up to 67% and 68%, respectively, compared to regular enterprise solid state drives.
ISSN:1539-9087
1558-3465
DOI:10.1145/3528577