Mathieu Faverge

1.3k total citations
25 papers, 491 citations indexed

About

Mathieu Faverge is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computational Theory and Mathematics. According to data from OpenAlex, Mathieu Faverge has authored 25 papers receiving a total of 491 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Hardware and Architecture, 20 papers in Computer Networks and Communications and 9 papers in Computational Theory and Mathematics. Recurrent topics in Mathieu Faverge's work include Parallel Computing and Optimization Techniques (22 papers), Distributed and Parallel Computing Systems (13 papers) and Matrix Theory and Algorithms (8 papers). Mathieu Faverge is often cited by papers focused on Parallel Computing and Optimization Techniques (22 papers), Distributed and Parallel Computing Systems (13 papers) and Matrix Theory and Algorithms (8 papers). Mathieu Faverge collaborates with scholars based in France, United States and United Kingdom. Mathieu Faverge's co-authors include Jack Dongarra, Thomas Hérault, Anthony Danalis, Aurélien Bouteiller, George Bosilca, Hatem Ltaief, Piotr Łuszczek, Jakub Kurzak, Emmanuel Agullo and Samuel Thibault and has published in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems, SIAM Journal on Matrix Analysis and Applications and Journal of Parallel and Distributed Computing.

In The Last Decade

Mathieu Faverge

23 papers receiving 472 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mathieu Faverge France 10 389 348 118 79 49 25 491
Asim YarKhan United States 14 396 1.0× 411 1.2× 174 1.5× 115 1.5× 48 1.0× 35 567
Jim Demmel United States 6 470 1.2× 348 1.0× 75 0.6× 148 1.9× 93 1.9× 9 584
Ernie Chan United States 12 435 1.1× 401 1.2× 70 0.6× 121 1.5× 125 2.6× 15 609
Amik Singh United States 6 409 1.1× 348 1.0× 39 0.3× 129 1.6× 88 1.8× 6 507
Pierre‐André Wacrenier France 4 662 1.7× 630 1.8× 279 2.4× 37 0.5× 37 0.8× 9 767
Cédric Augonnet France 7 807 2.1× 758 2.2× 322 2.7× 58 0.7× 60 1.2× 12 945
Eun-Jin Im South Korea 7 270 0.7× 227 0.7× 27 0.2× 75 0.9× 42 0.9× 14 361
David Wonnacott United States 14 392 1.0× 334 1.0× 123 1.0× 98 1.2× 158 3.2× 33 700
Fabrice Rastello France 11 351 0.9× 307 0.9× 67 0.6× 53 0.7× 74 1.5× 50 459
Simon David Hammond United States 12 259 0.7× 268 0.8× 81 0.7× 25 0.3× 76 1.6× 53 439

Countries citing papers authored by Mathieu Faverge

Since Specialization
Citations

This map shows the geographic impact of Mathieu Faverge's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mathieu Faverge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathieu Faverge more than expected).

Fields of papers citing papers by Mathieu Faverge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathieu Faverge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mathieu Faverge. The network helps show where Mathieu Faverge may publish in the future.

Co-authorship network of co-authors of Mathieu Faverge

This figure shows the co-authorship network connecting the top 25 collaborators of Mathieu Faverge. A scholar is included among the top collaborators of Mathieu Faverge based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mathieu Faverge. Mathieu Faverge is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Bramas, Bérenger, et al.. (2024). Dynamic Tasks Scheduling with Multiple Priorities on Heterogeneous Computing Systems. SPIRE - Sciences Po Institutional REpository. 31–40. 3 indexed citations
3.
Faverge, Mathieu, Nathalie Furmento, Abdou Guermouche, et al.. (2023). Programming heterogeneous architectures using hierarchical tasks. Concurrency and Computation Practice and Experience. 35(25). 3 indexed citations
4.
Agullo, Emmanuel, Alfredo Buttari, Olivier Coulaud, et al.. (2023). On the Arithmetic Intensity of Distributed-Memory Dense Matrix Multiplication Involving a Symmetric Input Matrix (SYMM). 357–367. 2 indexed citations
5.
Faverge, Mathieu, et al.. (2020). Tiled Algorithms for Efficient Task-Parallel ℌ-Matrix Solvers. 757–766. 3 indexed citations
6.
Darve, Eric, et al.. (2018). Sparse supernodal solver using block low-rank compression: Design, performance and analysis. Journal of Computational Science. 27. 255–270.
7.
Jagode, Heike, et al.. (2018). Evaluation of dataflow programming models for electronic structure theory. Concurrency and Computation Practice and Experience. 30(17). 1 indexed citations
8.
Faverge, Mathieu, et al.. (2017). Reordering Strategy for Blocking Optimization in Sparse Linear Solvers. SIAM Journal on Matrix Analysis and Applications. 38(1). 226–248. 8 indexed citations
9.
Agullo, Emmanuel, et al.. (2017). Achieving High Performance on Supercomputers with a Sequential Task-based Programming Model. IEEE Transactions on Parallel and Distributed Systems. 1–1. 46 indexed citations
10.
Ltaief, Hatem, et al.. (2017). Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures. IEEE Transactions on Parallel and Distributed Systems. 29(2). 312–323. 9 indexed citations
11.
Faverge, Mathieu, et al.. (2016). Stratégie de renumérotation pour optimiser la granularité des calculs dans la résolution des systèmes linéaires creux. HAL (Le Centre pour la Communication Scientifique Directe). 4 indexed citations
12.
Faverge, Mathieu, et al.. (2015). Mixing LU and QR factorization algorithms to design high-performance dense linear algebra solvers. Journal of Parallel and Distributed Computing. 85. 32–46. 6 indexed citations
13.
Dongarra, Jack, Mathieu Faverge, Mark Gates, et al.. (2014). A survey of recent developments in parallel implementations of Gaussian elimination. Concurrency and Computation Practice and Experience. 27(5). 1292–1309. 21 indexed citations
14.
Dongarra, Jack, Mathieu Faverge, Hatem Ltaief, & Piotr Łuszczek. (2013). Achieving numerical accuracy and high performance using recursive tile LU factorization with partial pivoting. Concurrency and Computation Practice and Experience. 26(7). 1408–1431. 20 indexed citations
15.
Kurzak, Jakub, Piotr Łuszczek, Mathieu Faverge, & Jack Dongarra. (2012). LU Factorization with Partial Pivoting for a Multicore System with Accelerators. IEEE Transactions on Parallel and Distributed Systems. 24(8). 1613–1621. 27 indexed citations
16.
Agullo, Emmanuel, George Bosilca, Bérenger Bramas, et al.. (2012). Poster: Matrices over Runtime Systems at Exascale. 1332–1332. 2 indexed citations
17.
Bosilca, George, Aurélien Bouteiller, Anthony Danalis, et al.. (2011). Flexible Development of Dense Linear Algebra Algorithms on Massively Parallel Architectures with DPLASMA. 1432–1441. 73 indexed citations
18.
Trahay, François, et al.. (2011). EZTrace: A Generic Framework for Performance Analysis. 618–619. 16 indexed citations
19.
Faverge, Mathieu, et al.. (2008). Dynamic scheduling for sparse direct solver on NUMA architectures. HAL (Le Centre pour la Communication Scientifique Directe). 2 indexed citations
20.
Faverge, Mathieu, et al.. (2008). A NUMA Aware Scheduler for a Parallel Sparse Direct Solver. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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