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Gloria Doci

dblp:348/4796 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Query processing and optimization › join processing
distributed join
0.712023
Distributed GPU Joins on Fast RDMA-capable Networks · Proc. ACM Manag. Data 2023
Query processing and optimization › join processing › parallel join
GPU join
0.712023
Distributed GPU Joins on Fast RDMA-capable Networks · Proc. ACM Manag. Data 2023
GPUs and heterogeneous computing
GPU computing
0.712023
Distributed GPU Joins on Fast RDMA-capable Networks · Proc. ACM Manag. Data 2023
Datacenter networks
RDMA
0.212023
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
YearPublicationVenuePosition
2023 Distributed GPU Joins on Fast RDMA-capable Networks
abstract
In 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. Data2