Everest: GPU-Accelerated System For Mining Temporal Motifs
Summary: Everest compiles temporal-motif mining (counting & enumeration) to GPUs, emitting motif-specific kernels and primitives to reduce memory latency and thread divergence. Adds lightweight load balancing and edge-partitioning to avoid inter-GPU comms, supports expressive temporal motifs and achieves ≈19× speedup. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yichao Yuan
- 2. Haojie Ye
- 3. Sanketh Vedula
- 4. Wynn Kaza
- 5. Nishil Talati
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,258 | TIMEST: Temporal Information Motif Estimator Using Sampling Trees | 2026 | VLDB | 4.1905499e-05 |
| 10,854 | Mayura: Exploiting Similarities in Motifs for Temporal Co-Mining | 2025 | VLDB | 4.1905499e-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 |
|---|---|---|---|---|
| 2,933 | Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU | 2020 | VLDB | 7.8633648e-05 |
| 3,959 | 2SCENT: An Efficient Algorithm for Enumerating All Simple Temporal Cycles | 2018 | VLDB | 6.583977e-05 |
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