Everest: GPU-Accelerated System For Mining Temporal Motifs
Summary: Everest compiles expressive temporal-motif queries into GPU-specialized plans for enumeration and counting, reducing memory latency and divergence. Load balancing and communication-free multi-GPU partitioning yield a 19× average speedup over a GPU baseline. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Yichao Yuan (University of Michigan)
- 2. Haojie Ye (University of Michigan)
- 3. Sanketh Vedula (Technion)
- 4. Wynn Kaza (University of Michigan)
- 5. Nishil Talati (University of Michigan)
BibTeX Citation
@article{yuan_vldb24,
title = {{Everest: GPU-Accelerated System For Mining Temporal Motifs}},
author = {Yuan, Yichao and Ye, Haojie and Vedula, Sanketh and Kaza, Wynn and Talati, Nishil},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {2},
pages = {162--174},
doi = {10.14778/3626292.3626299},
url = {https://doi.org/10.14778/3626292.3626299},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,546 | TIMEST: Temporal Information Motif Estimator Using Sampling Trees | 2026 | VLDB | 5.093636e-05 |
| 11,074 | Mayura: Exploiting Similarities in Motifs for Temporal Co-Mining | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,288 | Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU | 2020 | VLDB | 8.8025299e-05 |
| 3,955 | 2SCENT: An Efficient Algorithm for Enumerating All Simple Temporal Cycles | 2018 | VLDB | 6.9950293e-05 |
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