Demo Data from VLDB 2026 / reviewers in the wild / expert
Andre Luiz Rocha Tupinamba
dblp:190/0842
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author
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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 56% Distributed systems · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing › multi-GPU computing
distributed GPU computing |
0.2 | 1 | 2016 | Transparent and Optimized Distributed Processing on GPUs · IEEE Trans. Parallel Distributed Syst. 2016 |
Distributed systems
middleware |
0.2 | 1 | 2016 | Transparent and Optimized Distributed Processing on GPUs · IEEE Trans. Parallel Distributed Syst. 2016 |
GPUs and heterogeneous computing › heterogeneous programming models
OpenCL |
0.1 | 1 | 2016 | Transparent and Optimized Distributed Processing on GPUs · IEEE Trans. Parallel Distributed Syst. 2016 |
Methods — techniques the papers use, named apart from their topics
network optimization · 0.2batching · 0.2asynchronous communication · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Transparent and Optimized Distributed Processing on GPUsabstractDistributedCL is a middleware which enables transparent parallel processing on distributed GPUs. With the support of the DistributedCL middleware an application designed to use the OpenCL API can run in a distributed manner and transparently use remote GPUs without having to change or rebuild the code. The proposed architecture for the DistributedCL middleware is modular, with well-defined layers. A prototype was built according to the architecture, which considered various optimization points, including sending data in batches, network asynchronous communication and asynchronous request to the OpenCL API. The prototype was evaluated using available benchmarks and a specific benchmark, the CLBench, was developed to facilitate the evaluations according to the amount of processed data. The prototype presented good performance, higher when compared to similar proposals, which also consider transparent use of remote GPUs. The data size to be transmitted over the network was the major limiting factor. Andre Luiz Rocha Tupinamba, Alexandre Sztajnberg |
IEEE Trans. Parallel Distributed Syst. | 1 |