Events in April–May 2019
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AprilApril 1, 2019 |
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April 4, 2019(1 event)
keynote LIGkeynote LIG – |
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April 11, 2019(1 event)
Modélisation et implémentation du produit de matrice parallèle avec minimisation des communications, by Thomas LambertModélisation et implémentation du produit de matrice parallèle avec minimisation des communications, by Thomas Lambert – Avec l’émergence du calcul haute-performance (HPC) et des applications Big Data, de nouvelles problématiques cruciales sont apparues. Parmi elles on trouve le problème du transfert de données, c’est-à-dire des communications entre machines, qui peut générer des délais lors de gros calculs en plus d’avoir un impact sur la consommation énergétique. Dans cette présentation nous nous intéresserons à la réduction des communications pour un problème particulier : le produit de matrices. Dans un premier temps nous nous intéressons à eux modélisation théoriques, basées respectivement sur le partitionnement d'un carré et d'un cube, ainsi qu'au algorithmes d’approximations existant pour résoudre ce problème. Dans un second temps nous nous intéresserons à la mise en application de ces algorithmes avec une implémentation pratique du produit de matrice sur une plate-forme hétérogène. Bâtiment IMAG (442) |
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MayMay 1, 2019 |
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May 9, 2019(1 event)
One can only gain by replacing EASY Backfilling: A simple scheduling policies case study, by Salah Zrigui (Datamove)One can only gain by replacing EASY Backfilling: A simple scheduling policies case study, by Salah Zrigui (Datamove) – High-Performance Computing (HPC) platforms are salle 442 |
May 10, 2019
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May 16, 2019(1 event)
Using Big Data Solutions to Improve HPC systems, by Thomas Ropars (LIG)Using Big Data Solutions to Improve HPC systems, by Thomas Ropars (LIG) – Supercomputers are producing large amount of data that need to be analyzed. They produce mostly two kinds of data: scientific data and monitoring data. Scientific data are the results of the execution of numerical simulations and need to be analyzed to extract knowledge. Monitoring data are produced by all kinds of sensors and software components, and can be analyzed to detect, among other things, reliability and performance issues. Considering the scale of such systems, the amount of data to process is huge and analyzing these data with short response time is often necessary. Using techniques and algorithms coming from the Big Data community seems appealing in this context. This talk will present some of our efforts in trying to apply Big Data and Machine Learning techniques in the HPC context. It will cover 3 main topics: i) The use of Apache Spark Streaming as a tool for in-situ data analysis; ii) The analysis of time series to predict CPU overheating issues in Supercomputers; iii) The application of classification algorithms to the placement problem in NUMA platforms. Bâtiment IMAG (206) |
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May 21, 2019(1 event)
Modeling, Prediction and Optimization of Energy Consumption of MPI Applications using SimGrid, by Christian Heinrich (PhD defense)Modeling, Prediction and Optimization of Energy Consumption of MPI Applications using SimGrid, by Christian Heinrich (PhD defense) – The High-Performance Computing (HPC) community is currently undergoing The energy consumption of these machines will continue to grow in the One approach to predict energy consumption is
Committee:
Bâtiment IMAG (amphitheater) Saint-Martin-d'Hères, 38400 France |
May 22, 2019
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May 23, 2019(1 event)
Polaris daysPolaris days N/A |
May 24, 2019(1 event)
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JuneJune 1, 2019 |
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- April 3, 2024 @ Bâtiment IMAG (442) -- [Seminar] Victor Boone
Who: Victor Boone
When: Wednesday, April 3, 14:00-15:00
Where: 447
What: Learning MDPs with Extended Bellman Operators
More: Efficiently learning Markov Decision Processes (MDPs) is difficult. When facing an unknown environment, where is the adequate limit between repeating actions that have shown their efficiency in the past (exploitation of your knowledge) and testing alternatives that may actually be better than what you currently believe (exploration of the environment)? To bypass this dilemma, a well-known solution is the "optimism-in-face-of-uncertainty" principle: Think of the score of an action as being the largest that is statistically plausible.
The exploration-exploitation dilemma then becomes the problem of tuning optimism. In this talk, I will explain how optimism in MDPs can be all rephrased using a single operator, embedding all the uncertainty in your environment within a single MDP. This is a story about "extended Bellman operators" and "extended MDPs", and about how one can achieve minimax optimal regret using this machinery.
- April 11, 2024 @ Bâtiment IMAG (442) -- [Seminar] Charles Arnal
Who: Charles Arnal
When: Thursday, April 11, 14:00-15:00
Where: 442
What: Mode Estimation with Partial Feedback
More: The combination of lightly supervised pre-training and online fine-tuning has played a key role in recent AI developments. These new learning pipelines call for new theoretical frameworks. In this paper, we formalize core aspects of weakly supervised and active learning with a simple problem: the estimation of the mode of a distribution using partial feedback. We show how entropy coding allows for optimal information acquisition from partial feedback, develop coarse sufficient statistics for mode identification, and adapt bandit algorithms to our new setting. Finally, we combine those contributions into a statistically and computationally efficient solution to our problem.
- April 30, 2024 @ Bâtiment IMAG (442) -- Seminar Rémi Castera
Correlation of Rankings in Matching Markets