Demo Data from EDBT 2026 / reviewers in the wild / expert
Nuria Losada
dblp:153/7983
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › data distribution
data redistribution |
0.6 | 1 | 2022 | Evaluating Data Redistribution in PaRSEC · IEEE Trans. Parallel Distributed Syst. 2022 |
Parallel and multicore computing › parallel programming runtimes
task-based runtime |
0.6 | 1 | 2022 | Evaluating Data Redistribution in PaRSEC · IEEE Trans. Parallel Distributed Syst. 2022 |
Parallel and multicore computing
load balancing |
0.2 | 1 | 2022 | Evaluating Data Redistribution in PaRSEC · IEEE Trans. Parallel Distributed Syst. 2022 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Evaluating Data Redistribution in PaRSECabstractData 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 ApplicationsabstractDespite 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 |
PDP | 1 |