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Andre Luiz Rocha Tupinamba

dblp:190/0842 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing › multi-GPU computing
distributed GPU computing
0.212016
Transparent and Optimized Distributed Processing on GPUs · IEEE Trans. Parallel Distributed Syst. 2016
Distributed systems
middleware
0.212016
Transparent and Optimized Distributed Processing on GPUs · IEEE Trans. Parallel Distributed Syst. 2016
GPUs and heterogeneous computing › heterogeneous programming models
OpenCL
0.112016
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
YearPublicationVenuePosition
2016 Transparent and Optimized Distributed Processing on GPUs
abstract
DistributedCL 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