Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Nuria Losada

dblp:153/7983 · DBLP profile ↗
← Back
7ranked-venue papers
6as first author
1since 2021 · last 2022
0000-0002-1327-0478ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 6 · 5 first-author · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › data distribution
data redistribution
0.612022
Evaluating Data Redistribution in PaRSEC · IEEE Trans. Parallel Distributed Syst. 2022
Parallel and multicore computing › parallel programming runtimes
task-based runtime
0.612022
Evaluating Data Redistribution in PaRSEC · IEEE Trans. Parallel Distributed Syst. 2022
Parallel and multicore computing
load balancing
0.212022
Evaluating Data Redistribution in PaRSEC · IEEE Trans. Parallel Distributed Syst. 2022
YearPublicationVenuePosition
2022 Evaluating Data Redistribution in PaRSEC
abstract
Data redistribution aims to reshuffle data to optimize some objective for an algorithm. The objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal of increasing the efficiency and therefore reducing the time-to-solution for the algorithm. The classic redistribution problem focuses on optimally scheduling communications when reshuffling data between two regular, usually block-cyclic, data distributions. Besides distribution, data size is also a performance-critical parameter because it affects the reshuffling algorithm in terms of cache, communication efficiency, and potential parallelism. In addition, task-based runtime systems have gained popularity recently as a potential candidate to address the programming complexity on the way to exascale. In this scenario, it becomes paramount to develop a flexible redistribution algorithm for task-based runtime systems, which could support all types of regular and irregular data distributions and take data size into account. In this article, we detail a flexible redistribution algorithm and implement an efficient approach in a task-based runtime system,PaRSEC. Performance results show great capability compared to the theoreticalboundandScaLAPACK, and applications highlight an increased efficiency with little overhead in terms of data distribution, data size, and data format.
Qinglei Cao, George Bosilca, Nuria Losada, Wei Wu 0016, Dong Zhong, Jack J. Dongarra
IEEE Trans. Parallel Distributed Syst.3
2020 Fault tolerance of MPI applications in exascale systems: The ULFM solution
Nuria Losada, Patricia González, María J. Martín, George Bosilca, Aurelien Bouteiller, Keita Teranishi
Future Gener. Comput. Syst.1
2019 Local rollback for resilient MPI applications with application-level checkpointing and message logging
Nuria Losada, George Bosilca, Aurelien Bouteiller, Patricia González, María J. Martín
Future Gener. Comput. Syst.1
2017 A portable and adaptable fault tolerance solution for heterogeneous applications
Nuria Losada, Basilio B. Fraguela, Patricia González, María J. Martín
J. Parallel Distributed Comput.1
2017 Resilient MPI applications using an application-level checkpointing framework and ULFM
Nuria Losada, Iván Cores, María J. Martín, Patricia González
J. Supercomput.1
2017 Assessing resilient versus stop-and-restart fault-tolerant solutions in MPI applications
Nuria Losada, María J. Martín, Patricia González
J. Supercomput.1
2015 I/O Optimization in the Checkpointing of OpenMP Parallel Applications
abstract
Despite the increasing popularity of shared-memory systems, there is a lack of tools for providing fault tolerance support to shared-memory applications. Check pointing is one of the most popular fault tolerance techniques. However, check pointing cost in terms of computing time, network utilization or storage resources can be a limitation for its practical use. This work proposes different techniques for the optimization of the I/O cost in the check pointing of shared-memory parallel applications. The proposals are extensively evaluated using the OpenMP NAS Parallel Benchmarks. Results show a significant decrease of the check pointing overhead.
Nuria Losada, María J. Martín, Gabriel Rodríguez 0001, Patricia González
PDP1