-
News
- Journée au vert POLARIS 2022/05/23
- DATAMOVE/POLARIS picnic 2021/06/22
- DATAMOVE/POLARIS BBQ 2019 2019/06/14
- POLARIS Bootcamp (May 2019) 2019/05/24
- slides of Andras Gyorgy 2016/01/15
Next seminars
Events
Events in November–December 2017
MMonday TTuesday WWednesday TThursday FFriday SSaturday SSunday 30October 30, 201731October 31, 2017November
1November 1, 20172November 2, 20173November 3, 20174November 4, 20175November 5, 2017Journée de rentrée commune POLARIS/DATAMOVE
–
November 6, 2017Bâtiment IMAG (amphitheater)Saint-Martin-d'Hères, 38400France7November 7, 20178November 8, 2017Keynote LIG: George Wright - State of the Art Media Research
10November 10, 201711November 11, 201712November 12, 201713November 13, 201714November 14, 201715November 15, 2017Detecting Performance Outliers for Task-based HPC Applications in multi-[CPU|GPU|Node] clusters By Lucas Schnorr (Porto Allegre)
–
November 16, 2017Detecting Performance Outliers for Task-based HPC Applications in
multi-[CPU|GPU|Node] clustersProgramming paradigms in High-Performance Computing have
been shifting towards task-based models which are capable of
adapting readily to heterogeneous and scalable
supercomputers. Detecting performance outliers in such environments
is particularly difficult because it must consider architecture
heterogeneity and variability. In this work we present how we have
employed a very simple performance model to highlight task outliers
of the well-known tiled-based dense Cholesky factorization running
on top of StarPU-MPI, a runtime for task-based applications. Such
work has been integrated into our visualization framework based on the
R programming language and the tidyverse meta-package. Experiments
have been conducted in a controlled environment using the Chifflet
cluster at Lille, part of the Grid'5000 infrastructure, using up to
eight nodes, each one equipped with 28 cores and two GPUs. The
preliminary results, derived from collected traces, indicate that
explicit binding for the MPI and GPU-managing threads, within
StarPU, alleviate the issue, leading to performance gains.Bâtiment IMAG (406)Saint-Martin-d'Hères, 38400France17November 17, 201718November 18, 201719November 19, 201720November 20, 201721November 21, 201722November 22, 2017Inria 50th year Celebration
–
November 23, 201724November 24, 201725November 25, 201726November 26, 201727November 27, 201728November 28, 201729November 29, 2017Convergence d’algorithme de non regret, Amélie Heliou (Polaris)
–
November 30, 2017Les algorithmes de non-regret sont souvent utilisés dans les jeux répétés où les joueurs ont peu d’information sur le jeu auquel ils jouent. Ces algorithmes garantissent que le regret de chaque joueur est sous-linéaire. La moyenne temporelle des stratégies choisies en suivant un algorithme de non-regret converge dans l’ensemble des équilibres corrélés. Cependant cela ne donne aucune information sur la convergence de la séquence de stratégies.
Nous nous sommes intéressés à la question « est-ce que la sequence de stratégie obtenue pas un algorithme de non regret converge vers un équilibre de Nash? ».
Dans cet exposé, je présenterai un algorithme de non regret appelé Hedge qui est une version d’algorithmes à poids exponentiels. En particulier, je discuterai la convergence des séquences de stratégies obtenues par Hedge en utilisant deux types d’informations accessibles aux joueurs.Bâtiment IMAG (442)December
1December 1, 20172December 2, 20173December 3, 20174December 4, 20175December 5, 20176December 6, 2017Keynote
December 7, 2017
8December 8, 20179December 9, 201710December 10, 201711December 11, 201712December 12, 201713December 13, 2017Learning efficient Nash equilibra in distributed systems by Bary Pradelski (ETH Zurich)
–
December 14, 2017Learning efficient Nash equilibra in distributed systems
with H. Peyton Young
An individual’s learning rule is completely uncoupled if it does not depend directly on the actions or payoffs of anyone else. We propose a variant of log linear learning that is completely uncoupled and that selects an efficient (welfare-maximizing) pure Nash equilibrium in all generic n-person games that possess at least one pure Nash equilibrium. In games that do not have such an equilibrium, there is a simple formula that expresses the long-run probability of the various disequilibrium states in terms of two factors: i) the sum of payoffs over all agents, and ii) the maximum payoff gain that results from a unilateral deviation by some agent. This welfare/stability trade-off criterion provides a novel framework for analyzing the selection of disequilibrium as well as equilibrium states in n-person games.
Bâtiment IMAG (306)Saint-Martin-d'Hères, 38400France15December 15, 201716December 16, 201717December 17, 2017Autotuning MPI Collectives using Performance Guidelines, Sascha Hunold
–
December 18, 2017MPI collective operations provide a standardized interface for performing data movements within a group of processes. The efficiency
of collective communication operations depends on the actual algorithm, its implementation, and the specific communication problem
(type of communication, message size, and number of processes).
Many MPI libraries provide numerous algorithms for specific collective operations. The strategy for selecting an efficient algorithm
is often times predefined (hard-coded) in MPI libraries, but some of
them, such as Open MPI, allow users to change the algorithm manually. Finding the best algorithm for each case is a hard problem, and
several approaches to tune these algorithmic parameters have been
proposed. We use an orthogonal approach to the parameter-tuning
of MPI collectives, that is, instead of testing individual algorithmic
choices provided by an MPI library, we compare the latency of
a specific MPI collective operation to the latency of semantically
equivalent functions, which we call the mock-up implementations.
The structure of the mock-up implementations is defined by selfconsistent performance guidelines. The advantage of this approach
is that tuning using mock-up implementations is always possible,
whether or not an MPI library allows users to select a specific algorithm at run-time. We implement this concept in a library called
PGMPITuneLib, which is layered between the user code and the
actual MPI implementation. This library selects the best-performing
algorithmic pattern of an MPI collective by intercepting MPI calls
and redirecting them to our mock-up implementations. Experimental results show that PGMPITuneLib can significantly reduce the
latency of MPI collectives, and also equally important, that it can
help identifying the tuning potential of MPI libraries.19December 19, 201720December 20, 2017TAPIOCA : Une bibliothèque d'agrégation de données pour les I/O parallèles prenant en compte la topologie, François Tessier, Argonne
–
December 21, 2017TAPIOCA : Une bibliothèque d'agrégation de données pour les I/O
parallèles prenant en compte la topologieL'augmentation de la puissance de calcul des supercalculateurs engendre
un coût considérable des mouvements de données. En outre, la majorité
des simulations scientifiques ont des besoins importants en terme de
lecture et d'écriture sur les systèmes de fichiers parallèles. De
nombreuses solutions logicielles ont été développées pour contenir le
goulot d'étranglement causé par les I/O. Une stratégie bien connue dans
le monde des opérations collectives d'I/O consiste à sélectionner un
sous-ensemble des processus de l'application pour agréger des morceaux
de données contiguës avant d'effectuer les lectures et écritures. Dans
cet exposé, je présenterai TAPIOCA, une bibliothèque MPI implémentant un
algorithme d’agrégation de données optimisé prenant en compte la
topologie. Je montrerai les gains de performance substantiels en lecture
et écriture que nous avons obtenus sur deux supercalculateurs présents à
Argonne National Laboratory. Pour terminer, j'aborderai nos travaux
actuels dans TAPIOCA afin de tirer parti des nouveaux niveaux de mémoire
et de stockage disponibles sur les systèmes actuels et à venir (MCDRAM,
SSD locaux, ...).Bâtiment IMAGSaint-Martin-d'Hères, 38400France22December 22, 201723December 23, 201724December 24, 201725December 25, 201726December 26, 201727December 27, 201728December 28, 201729December 29, 201730December 30, 201731December 31, 2017Meta
Performance evaluation and Optimization of LARge Infrastructures and Systems
