Persistent Spiral Storage

The advent of byte-addressable persistent memory (PM) has led to a resurgence of interest in adapting existing dynamic hashing schemes to PM. Compared with its two well-known peers (extendible hashing and linear hashing), spiral storage has received little attention due to its limitations. After an in-depth analysis, however, we discover that it has a good potential for PM. To show its strength, we develop a persistent spiral storage called PASS (Persistence-Aware Spiral Storage), which is facilitated by a group of new/existing techniques. Further, we conduct a comprehensive evaluation of PASS on a server equipped with Intel Optane DC Persistent Memory Modules (DCPMM). Experimental results demonstrate that compared with two state-of-the-art schemes it exhibits better performance.

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Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/iccd63220.2024.00046
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
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article

Persistent Spiral Storage

Wenyu Peng, Paul H. Siegel, Tao Xie
Parallel Computing and Optimization Techniques
article

Persistent Spiral Storage

Wenyu Peng, Paul H. Siegel, Tao Xie
article en

Abstract

The advent of byte-addressable persistent memory (PM) has led to a resurgence of interest in adapting existing dynamic hashing schemes to PM. Compared with its two well-known peers (extendible hashing and linear hashing), spiral storage has received little attention due to its limitations. After an in-depth analysis, however, we discover that it has a good potential for PM. To show its strength, we develop a persistent spiral storage called PASS (Persistence-Aware Spiral Storage), which is facilitated by a group of new/existing techniques. Further, we conduct a comprehensive evaluation of PASS on a server equipped with Intel Optane DC Persistent Memory Modules (DCPMM). Experimental results demonstrate that compared with two state-of-the-art schemes it exhibits better performance.

San Diego State University (US), University of California San Diego (US)
Openalex Percentile: Top 73%
Parallel Computing and Optimization Techniques
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