Asset Details
Do you wish to reserve the book?
A quantitative evaluation of persistent memory hash indexes
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A quantitative evaluation of persistent memory hash indexes
Do you wish to request the book?
A quantitative evaluation of persistent memory hash indexes
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A quantitative evaluation of persistent memory hash indexes
2024
Overview
Persistent memory (PMem) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery. Especially with the release of Intel Optane DC Persistent Memory Modules, we have witnessed a flourish in (re)designing persistent hash indexes. However, most of them are focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how the proposed hash indexes perform under a unified testing framework and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of several state-of-the-art hash tables including CCEH, Dash, PCLHT, Clevel, Viper, Halo, SOFT, and Plush, with the second-generation PMem hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PMem bandwidth, CPU instructions, and NUMA) affect the performance of PMem-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts and suggest desirable optimizations and directions for the future development of PMem-based hash tables.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
