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ORCIDMihail Popov
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Books and Theses
- 2016
[b1]Mihail Popov:
Automatic Decomposition of Parallel Programs for Optimization and Performance Prediction. (Décomposition Automatique des Programmes Parallèles pour l'Optimisation et la Prédiction de Performance). Versailles Saint-Quentin-en-Yvelines University, France, 2016
Journal Articles
- 2023
- [j4]Lana Scravaglieri, Mihail Popov
, Laércio Lima Pilla, Amina Guermouche, Olivier Aumage, Emmanuelle Saillard:
Optimizing performance and energy across problem sizes through a search space exploration and machine learning. J. Parallel Distributed Comput. 180: 104720 (2023) - 2018
- [j3]William Jalby, David J. Kuck, Allen D. Malony, Michel Masella, Abdelhafid Mazouz, Mihail Popov:
The Long and Winding Road Toward Efficient High-Performance Computing. Proc. IEEE 106(11): 1985-2003 (2018) - 2017
- [j2]Mihail Popov, Chadi Akel, Yohan Chatelain, William Jalby, Pablo de Oliveira Castro:
Piecewise holistic autotuning of parallel programs with CERE. Concurr. Comput. Pract. Exp. 29(15) (2017) - 2015
- [j1]Pablo de Oliveira Castro, Chadi Akel, Eric Petit, Mihail Popov, William Jalby:
CERE: LLVM-Based Codelet Extractor and REplayer for Piecewise Benchmarking and Optimization. ACM Trans. Archit. Code Optim. 12(1): 6:1-6:24 (2015)
Conference and Workshop Papers
- 2024
- [c8]Jad El Karchi, Hanze Chen, Ali TehraniJamsaz, Ali Jannesari, Mihail Popov, Emmanuelle Saillard:
MPI Errors Detection using GNN Embedding and Vector Embedding over LLVM IR. IPDPS 2024: 595-607 - 2022
- [c7]Ali TehraniJamsaz, Mihail Popov, Akash Dutta, Emmanuelle Saillard, Ali Jannesari:
Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization. IPDPS 2022: 1206-1216 - [c6]Marina Shimchenko, Mihail Popov, Tobias Wrigstad:
Analysing and Predicting Energy Consumption of Garbage Collectors in OpenJDK. MPLR 2022: 3-15 - 2020
- [c5]Isaac Sánchez Barrera, David Black-Schaffer, Marc Casas, Miquel Moretó, Anastasiia Stupnikova, Mihail Popov:
Modeling and optimizing NUMA effects and prefetching with machine learning. ICS 2020: 34:1-34:13 - 2019
- [c4]Mihail Popov, Alexandra Jimborean, David Black-Schaffer:
Efficient thread/page/parallelism autotuning for NUMA systems. ICS 2019: 342-353 - 2016
- [c3]Mihail Popov, Chadi Akel, William Jalby, Pablo de Oliveira Castro:
Piecewise Holistic Autotuning of Compiler and Runtime Parameters. Euro-Par 2016: 238-250 - 2015
- [c2]Mihail Popov, Chadi Akel, Florent Conti, William Jalby, Pablo de Oliveira Castro:
PCERE: Fine-Grained Parallel Benchmark Decomposition for Scalability Prediction. IPDPS 2015: 1151-1160 - 2014
- [c1]Pablo de Oliveira Castro, Yuriy Kashnikov, Chadi Akel, Mihail Popov, William Jalby:
Fine-grained Benchmark Subsetting for System Selection. CGO 2014: 132
Informal and Other Publications
- 2024
- [i2]Jad El Karchi, Hanze Chen, Ali TehraniJamsaz, Ali Jannesari, Mihail Popov, Emmanuelle Saillard:
MPI Errors Detection using GNN Embedding and Vector Embedding over LLVM IR. CoRR abs/2403.02518 (2024) - 2022
- [i1]Ali TehraniJamsaz, Mihail Popov, Akash Dutta, Emmanuelle Saillard, Ali Jannesari:
Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization. CoRR abs/2203.00611 (2022)
Coauthor Index
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