LearnedFTL: A Learning-Based Page-Level FTL for Reducing Double Reads in Flash-Based SSDs

Wang, Shengzhe; Lin, Zihang; Wu, Suzhen; Jiang, Hong; Zhang, Jie; Mao, Bo

doi:10.1109/hpca57654.2024.00054

2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA) 2024

DOI: 10.1109/hpca57654.2024.00054

|View full text |Cite

LearnedFTL: A Learning-Based Page-Level FTL for Reducing Double Reads in Flash-Based SSDs

Shengzhe Wang,

Zihang Lin,

Suzhen Wu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Accelerating String-Key Learned Index Structures via Memoization-Based Incremental Training

Kim,

Hwang,

Heo

et al. 2024

Proc. VLDB Endow.

View full text Add to dashboard Cite

Learned indexes use machine learning models to learn the mappings between keys and their corresponding positions in key-value indexes. These indexes use the mapping information as training data. Learned indexes require frequent retrainings of their models to incorporate the changes introduced by update queries. To efficiently retrain the models, existing learned index systems often harness a linear algebraic QR factorization technique that performs matrix decomposition. This factorization approach processes all key-position pairs during each retraining, resulting in compute operations that grow linearly with the total number of keys and their lengths. Consequently, the retrainings create a severe performance bottleneck, especially for variable-length string keys, while the retrainings are crucial for maintaining high prediction accuracy and in turn, ensuring low query service latency. To address this performance problem, we develop an algorithm-hardware co-designed string-key learned index system, dubbed SIA. In designing SIA, we leverage a unique algorithmic property of the matrix decomposition-based training method. Exploiting the property, we develop a memoization-based incremental training scheme, which only requires computation over updated keys, while decomposition results of non-updated keys from previous computations can be reused. We further enhance SIA to offload a portion of this training process to an FPGA accelerator to not only relieve CPU resources for serving index queries (i.e., inference), but also accelerate the training itself. Our evaluation shows that compared to ALEX, LIPP, and SIndex, a state-of-the-art learned index systems, SIA-accelerated learned indexes offer 2.6× and 3.4× higher throughput on the two real-world benchmark suites, YCSB and Twitter cache trace, respectively.

show abstract

Accelerating String-Key Learned Index Structures via Memoization-Based Incremental Training

Kim,

Hwang,

Heo

et al. 2024

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

LearnedFTL: A Learning-Based Page-Level FTL for Reducing Double Reads in Flash-Based SSDs

Cited by 1 publication

References 27 publications

Accelerating String-Key Learned Index Structures via Memoization-Based Incremental Training

Accelerating String-Key Learned Index Structures via Memoization-Based Incremental Training

Contact Info

Product

Resources

About