Approaching DRAM performance by using microsecond-latency flash memory for small-sized random read accesses: a new access method and its graph applications
Summary: Introduces a flash access method that combines optimized I/O issuance with stackless-coroutine contexts, reducing per-request CPU overhead below 10 ns and hiding microsecond latency. Graph workloads on large flash-resident datasets achieve 88–141% of all-DRAM execution time. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Tomoya Suzuki (Kioxia Corporation)
- 2. Kazuhiro Hiwada (Kioxia Corporation)
- 3. Hirotsugu Kajihara (Kioxia Corporation)
- 4. Shintaro Sano (Kioxia Corporation)
- 5. Shuou Nomura (Kioxia Corporation)
- 6. Tatsuo Shiozawa (Kioxia Corporation)
BibTeX Citation
@article{suzuki_vldb21,
title = {{Approaching DRAM performance by using microsecond-latency flash memory for small-sized random read accesses: a new access method and its graph applications}},
author = {Suzuki, Tomoya and Hiwada, Kazuhiro and Kajihara, Hirotsugu and Sano, Shintaro and Nomura, Shuou and Shiozawa, Tatsuo},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {8},
pages = {1311--1324},
doi = {10.14778/3457390.3457397},
url = {https://doi.org/10.14778/3457390.3457397},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,341 | Analysis and Evaluation of Using Microsecond-Latency Memory for In-Memory Indices and Caches in SSD-Based Key-Value Stores | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 2,767 | Exploiting Coroutines to Attack the "Killer Nanoseconds" | 2018 | VLDB | 8.1507611e-05 |
| 3,175 | Single Machine Graph Analytics on Massive Datasets Using Intel Optane DC Persistent Memory | 2020 | VLDB | 7.6656917e-05 |
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