KV-CSD: A Hardware-Accelerated Key-Value Store for Data-Intensive Applications

Park, Inhyuk; Zheng, Qing; Manno, Dominic; Yang, Soonyeal; Lee, Jason; Bonnie, David; Settlemyer, Bradley; Kim, Youngjae; Chung, Woosuk; Grider, Gary

doi:10.1109/cluster52292.2023.00019

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2024

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Cited by 3 publications

References 48 publications

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Enhancement of Convolutional Neural Network Hardware Accelerators Efficiency Using Sparsity Optimization Framework

Kurapati,

Ramachandran

2024

IEEE Access

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Enhancement of Convolutional Neural Network Hardware Accelerators Efficiency Using Sparsity Optimization Framework

Kurapati,

Ramachandran

2024

IEEE Access

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BandSlim: A Novel Bandwidth and Space-Efficient KV-SSD with an Escape-from-Block Approach

Park,

Lee,

Hwang

et al. 2024

Proceedings of the 53rd International Conference on Parallel Processing

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Storage Abstractions for SSDs: The Past, Present, and Future

Zhang,

Bhimani,

Pei

et al. 2024

ACM Trans. Storage

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This paper traces the evolution of SSD (solid-state drive) interfaces, examining the transition from the block storage paradigm inherited from hard disk drives to SSD-specific standards customized to flash memory. Early SSDs conformed to the block abstraction for compatibility with the existing software storage stack, but studies and deployments show that this limits the performance potential for SSDs. As a result, new SSD-specific interface standards emerged to not only capitalize on the low latency and abundant internal parallelism of SSDs, but also include new command sets that diverge from the longstanding block abstraction. We first describe flash memory technology in the context of the block storage abstraction and the components within an SSD that provide the block storage illusion. We then describe the genealogy and relationships among academic research and industry standardization efforts for SSDs, along with some of their rise and fall in popularity. We classify these work into four evolving branches: (1) extending block abstraction with host-SSD hints/directives; (2) enhancing host-level control over SSDs; (3) offloading host-level management to SSDs; and (4) making SSDs byte-addressable. By dissecting these trajectories, the paper also sheds light on the emerging challenges and opportunities, providing a roadmap for future research and development in SSD technologies.

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KV-CSD: A Hardware-Accelerated Key-Value Store for Data-Intensive Applications

Cited by 3 publications

References 48 publications

Enhancement of Convolutional Neural Network Hardware Accelerators Efficiency Using Sparsity Optimization Framework

Enhancement of Convolutional Neural Network Hardware Accelerators Efficiency Using Sparsity Optimization Framework

BandSlim: A Novel Bandwidth and Space-Efficient KV-SSD with an Escape-from-Block Approach

Storage Abstractions for SSDs: The Past, Present, and Future

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