April 4, 2019
keynote LIG
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April 4, 2019
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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
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1April 1, 20192April 2, 20193April 3, 2019keynote LIG–
April 4, 20195April 5, 20196April 6, 20197April 7, 20198April 8, 20199April 9, 201910April 10, 2019Modélisation et implémentation du produit de matrice parallèle avec minimisation des communications, by Thomas Lambert–
April 11, 2019Avec 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)12April 12, 201913April 13, 201914April 14, 201915April 15, 201916April 16, 201917April 17, 201918April 18, 201919April 19, 201920April 20, 201921April 21, 201922April 22, 201923April 23, 201924April 24, 201925April 25, 201926April 26, 201927April 27, 201928April 28, 201929April 29, 201930April 30, 2019May
1May 1, 20192May 2, 20193May 3, 20194May 4, 20195May 5, 20196May 6, 20197May 7, 20198May 8, 2019One can only gain by replacing EASY Backfilling: A simple scheduling policies case study, by Salah Zrigui (Datamove)–
May 9, 2019High-Performance Computing (HPC) platforms are
growing in size and complexity. In order to improve the quality
of service of such platforms, researchers are devoting a great
amount of effort to devise algorithms and techniques to improve
different aspects of performance such as energy consumption,
total usage of the platform, and fairness between users. In spite
of this, system administrators are always reluctant to deploy
state of the art scheduling methods and most of them revert
to EASY-backfilling, also known as EASY-FCFS (EASY-First-
Come-First-Served). Newer methods frequently are complex and
obscure and the simplicity and transparency of EASY are too
important to sacrifice.
In this work, we used execution logs from five HPC platforms
to compare four simple scheduling policies: FCFS, Shortest esti-
mated Processing time First (SPF), Smallest Requested Resources
First (SQF), and Smallest estimated Area First (SAF). Using
simulations, we performed a thorough analysis of the cumulative
results for up to 180 weeks and considered three scheduling
objectives: waiting time, slowdown and per-processor slowdown.
We also evaluated other effects, such as the relationship between
job size and slowdown, the distribution of slowdown values,
and the number of backfilled jobs, for each HPC platform and
scheduling policy.
We conclude that one can only gain by replacing EASY-
backfilling with SAF with backfilling, as it offers improvements
in performance by up to 80% in the slowdown metric while main-
taining the simplicity and the transparency of FCFS. Moreover,
SAF reduces the number of jobs with large slowdowns and the
inclusion of a simple thresholding mechanism guarantees that no
starvation occurs. Finally, we propose SAF as a new benchmark
for future scheduling studies.10May 10, 201911May 11, 201912May 12, 201913May 13, 201914May 14, 201915May 15, 2019Using Big Data Solutions to Improve HPC systems, by Thomas Ropars (LIG)–
May 16, 2019Supercomputers 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)17May 17, 201918May 18, 201919May 19, 201920May 20, 2019Modeling, Prediction and Optimization of Energy Consumption of MPI Applications using SimGrid, by Christian Heinrich (PhD defense)–
May 21, 2019The High-Performance Computing (HPC) community is currently undergoing
disruptive technology changes in almost all fields, including a switch
towards massive parallelism with several thousand compute cores on a
single GPU or accelerator and new, complex networks.The energy consumption of these machines will continue to grow in the
future, making energy one of the principal cost factors of machine
ownership. This explains why even the classic
metric "flop/s", generally used to evaluate HPC applications and
machines, is widely regarded as to be replaced by an energy-centric metric "flop/watt".One approach to predict energy consumption is
through simulation, however, an accurate simulation of the system is
crucial to estimate the energy faithfully. In this thesis, we
contribute to the performance and energy prediction of HPC architectures.
We propose an energy model which we have implemented in the open
source SimGrid simulator. We validate this model by carefully and
systematically comparing it with real experiments.
We leverage this contribution to both evaluate existing and propose
new DVFS governors that are particularly designed to suit the HPC context.Committee:
- Martin SCHULZ, Professor, Technical University of Munich
- Laurent LEFÈVRE, Research Scientist, Inria / ENS Lyon
- Amina GUERMOUCHE, Assistant Professor, Télécom SudParis
- Jean-François MÉHAUT, Professor, Grenoble-Alpes University
- Arnaud LEGRAND, Senior Research Scientist, CNRS, PhD Advisor
Bâtiment IMAG (amphitheater)Saint-Martin-d'Hères, 38400France22May 22, 2019Polaris daysN/A
May 23, 2019 – May 24, 2019Polaris daysN/A
May 23, 2019 – May 24, 201925May 25, 201926May 26, 201927May 27, 201928May 28, 201929May 29, 201930May 30, 201931May 31, 2019June
1June 1, 20192June 2, 2019Meta
