Demo Data from VLDB 2026 / reviewers in the wild / expert
Gloria Doci
dblp:348/4796
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0003-2193-2245ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Query processing and optimization · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 100% | |
| Computer networks
1 paper |
Datacenter networks · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Query processing and optimization › join processing
distributed join |
0.7 | 1 | 2023 | Distributed GPU Joins on Fast RDMA-capable Networks · Proc. ACM Manag. Data 2023 |
Query processing and optimization › join processing › parallel join
GPU join |
0.7 | 1 | 2023 | Distributed GPU Joins on Fast RDMA-capable Networks · Proc. ACM Manag. Data 2023 |
GPUs and heterogeneous computing
GPU computing |
0.7 | 1 | 2023 | Distributed GPU Joins on Fast RDMA-capable Networks · Proc. ACM Manag. Data 2023 |
Datacenter networks
RDMA |
0.2 | 1 | 2023 | Distributed GPU Joins on Fast RDMA-capable Networks · Proc. ACM Manag. Data 2023 |
Methods — techniques the papers use, named apart from their topics
pipelined join execution · 2.0network shuffling overlap · 2.0RDMA · 2.0GPUDirect · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Distributed GPU Joins on Fast RDMA-capable NetworksabstractIn this paper, we present a novel pipelined GPU join that accelerates the performance of distributed DBMSs by leveraging GPU resources on fast networks. A key insight is that we enable pipelined join execution by overlapping the network shuffling with the build and probe phases, thereby significantly reducing the GPU idle time. To demonstrate this, we propose novel algorithms for distributed pipelined GPU joins with RDMA and GPUDirect for both arbitrarily large probe- and build-side tables. In our evaluation, we show our pipelined distributed GPU join can reduce the overall runtime of a full query by up to 6× against a state-of-the-art CPU-only join. Lasse Thostrup, Gloria Doci, Nils Boeschen, Manisha Luthra, Carsten Binnig |
Proc. ACM Manag. Data | 2 |