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Main Topics | Schedule | Speaker | Affiliation | Type of presentation | Title (tentative) | Download |
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Dinner Before the Workshop | 7:30 PM | Only people registered for the dinner |
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Workshop Day 1 | Wednesday June 12th |
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| TITLES ARE TEMPORARY (except if in bold font) |
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Registration | 08:00 |
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Welcome and Introduction | 08:30 | Marc Snir + Franck Cappello | INRIA&UIUC&ANL | Background | Welcome, Workshop objectives and organization |
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| 08:45 | Thom Dunning | UIUC | Background | NCSA updates and vision of the collaboration |
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| 09:00 | Marc Snir | ANL | Background | ANL updates vision of the collaboration |
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| 09:15 | Frederic Desprez | Inria | Background | INRIA updates and vision of the collaboration |
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Big systems | 9:30 | Bill Kramer | UIUC | Background | Update on BlueWaters |
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| 10:00 | Break |
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| 10:30 | Mitsuhisa Sato | U. Tsukuba & AICS | Background | AICS and the K computer |
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| 11:00 | Paul Gibbon | Juelich | Background | TBA |
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Resilience&fault tolerance and simulation | 11:30 | Marc Snir | ANL&UIUC | Report | ICIS report on Resilience |
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| 12:00 | Lunch |
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Resilience&fault tolerance and simulation | 13:30 | Vincent Baudoui | Total & ANL | Joint-Results | TBA |
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| 14:00 | Bogdan Nicolae | IBM | Joint Result | ACM HPDC 2013 paper |
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| 14:30 | Martin Quison | INRIA | Result | Improving Simulations of MPI Applications Using A Hybrid Network Model with Topology and Contention Support | ||
Numerical Algorithms | 15:00 | Bill Gropp | UIUC | Background | TBA |
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| 15:30 | Break |
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16:00 | ANL | Background | TBA |
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| 16:30 | Frederic Nataf | INRIA&P6 | Background | Toward black-box adaptive domain decomposition methods |
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| 17:00 | Luke Olson | UIUC | Background | TBA |
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| 17:30 | Marc Baboulin | INRIA | Background | Using condition numbers to assess numerical quality in high-performance computing applications |
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| 18:00 | Adjourn |
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| 19:00 | Dinner |
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Workshop Day 2 | Thursday June 13th |
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Programming Models (cont.) | 08:30 | Jean-François Mehaut | INRIA | Result | Progresses in the European FP7 Mont-Blanc 1 project and objectives of its follow up: Mont-Blanc 2 |
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| 09:00 | Rajeev Thakur | ANL | Background | TBA |
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| 09:30 | Andra Ecaterina Hugo | INRIA | Results | TBA |
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| 10:00 | Celso Mendes | UIUC | Background | TBA |
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| 10:30 | Break |
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Big Data, I/O, Visualization | 11:00 | Dries Kimpe | ANL | Results | TBA |
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| 11:30 | Gilles Fedak | INRIA | Result | Active Data: A Programming Model to Manage Data Life Cycle Across Heterogeneous Systems and Infrastructures |
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| 12:00 | Matthieu Dorrier | INRIA | Joint Result | Data Analysis of Ensemble Simulations: an In Situ Approach using Damaris |
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| 12:30 | Ian Foster | ANL | Background | TBA |
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| 13:00 | Lunch |
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Mini Workshop1 |
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Resilience | 14:00 | Ana Gainaru | UIUC | Results | Failure prediction on Blue Waters |
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| 14:30 | Xiang Ni | UIUC | Results | TBA |
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| 15:00 | Tatiana | INRIA & ANL | Result | TBA |
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| 15:30 | Mohamed Slim Bouguerra | INRIA & ANL | Result | TBA |
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| 16:00 | Break |
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| 16:30 | Amina Guermouche | UVSQ | Result | Multi-criteria Checkpointing Strategies: Response-time versus Resource Utilization |
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| 17:00 | Thomas Ropars | EPFL | Result | TBA |
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| 17h30 | Mehdi Diouri | INRIA | Result | ECOFIT: A Framework to Estimate Energy Consumption of Fault Tolerance Protocols for HPC Applications |
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| 18:00 | Adjourn |
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Mini Workshop2 |
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Numerical Algorithms and Libraries | 14:00 | Laura Grigori | INRIA | Result | TBA |
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| 14:30 | Stefan Wild | ANL | Result | TBA |
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| 15:00 | Frederic Hecht | INRIA/P6 | Result | TBA |
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| 15:30 | Jed Brown | ANL | Result | TBA |
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| 16:00 | Break |
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| 16:30 | Yushan Wang | INRIA P11 | Result | TBA |
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| 17:00 | Jean Utke | ANL | Result | Designing and implementing a tool-indedendent, adjoinable MPI wrapper library |
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| 17:30 | Laurent Hascoet | INRIA | Result | The adjoint of MPI one-sided communications |
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| 18:00 | Adjourn |
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| 19:00 | Banquet |
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| Lyon |
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Workshop Day 3 | Friday June 14th |
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Mini Workshop1 (cont.) |
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Resilience | 08:30 | Di Sheng | INRIA | Result | TBA |
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| 09:00 | Guillaume Aupy | INRIA | Result | TBA |
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| 09:30 | Discussion |
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| 10:00 | Break |
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Mini Workshop3 | 10:30 | Guillaume Mercier | INRIA | Result | TBA |
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Programming and Scheduling | 11:00 | Vincent Lanore | INRIA | Result | TBA |
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| 11:30 | Anne Benoit | INRIA | Result | Energy-efficient scheduling |
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| 12:00 | François Tessier | INRIA | Result | TBA |
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| 12:30 | Discussions |
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| 13:00 | Closing and Lunch |
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Mini Workshop2 (cont.) |
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Numerical Algorithms and Libraries | 08:30 | François Pellegrini | INRIA | ResultTBA | Shared memory parallel algorithms in Scotch 6 |
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| 09:00 | Luc Giraud | INRIA | Result | TBA |
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| 09:30 | Discussions |
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| 10:00 | Break |
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Mini Workshop4 | 10:30 | Kate Keahey | ANL | Result | TBA |
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Clouds | 11:00 | Gabriel Antoniu | INRIA | Result | TBA |
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| 11:30 | Christian Perez | INRIA | Result | TBA |
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| 12:00 | Eddy Caron | INRIA | Result | TBA |
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| 12:30 | Discussions |
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| 13:00 | Closing and Lunch |
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Active Data: A Programming Model to Manage Data Life Cycle Across Heterogeneous Systems and Infrastructures
The Big Data challenge consists in managing, storing, analyzing and visualizing these huge and ever growing data sets to extract sense and knowledge. As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key point is to handle the complexity of the data life cycle, i.e. the various operations performed on data: transfer, archiving,
replication, deletion, etc. To alleviate the complexity of the data life cycle, we propose Active Data, a programming model to automate and improve the expressiveness of data management applications. We first introduce the concept of data life cycle and define a formal model that allow to expose data life cycle across heterogeneous systems and infrastructures. The Active Data
programming model allows code execution at each stage of the data life cycle: routines provided by programmers are executed when a set of events (creation, replication, transfer, deletion) happen to any data. We implement and evaluate the model with four use cases: a storage cache to Amazon-S3, a cooperative sensor network, an incremental implementation of the MapReduce
programming model and automated data provenance tracking across heterogeneous systems. Altogether, these scenarios illustrate the adequateness of the model to program applications that manage
distributed and dynamic data sets. We also show that applications that do not leverage on data life cycle can benefit from Active Data to improve their performances.
Francois Pellegrini
Shared memory parallel algorithms in Scotch 6
The Scotch software package comprises two libraries: the Scotch sequential library, and the PT-Scotch parallel library. The latter is based on a distributed memory paradigm, and uses MPI to exchange data between processes. The advent of many-core, shared memory, machines imposes to reconsider this approach. The complexity of graph partitioning algorithms is low compared to factorization. A first solution is to reduce communication overhead by running graph partitioning only on a limited number of nodes. A second solution is to make graph partitioning algorithms more efficient, by reducing communication overhead and resorting to shared memory parallelism. This talk will present our first experiments in this direction.