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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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Registration | 08:00 |
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Welcome and Introduction Amphitheatre | 08:30 | Marc Snir + Franck Cappello | INRIA&UIUC&ANL | Background | Welcome, Workshop objectives and organization | |||||||||
| 08:45 | Bill Kramer | UIUC | Background | NCSA updates and vision of the collaboration | |||||||||
| 09:00 | Marc Snir | ANL | Background | ANL updates vision of the collaboration | |||||||||
| 09:15 | Frederic Desprez | Inria | Background | INRIA updates and vision of the collaboration | Desprez-HPC@Inria-JLPC-0613.pdf | ||||||||
Big systems | 9:30 | Bill Kramer | UIUC | Background | Update on BlueWaters | BW Overview - Inria-Illinois Joint Workshop June 2013-v1.pdf | ||||||||
| 10:00 | Break |
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| 10:30 | Mitsuhisa Sato | U. Tsukuba & AICS | Background | AICS and the K computer |
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CANCELED | 11:00 | Paul Gibbon | Juelich | Background | Meeting the Exascale Challenge at the Juelich Supercomputing Centre. |
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Resilience&fault tolerance and simulation | 11:3000 | Marc Snir | ANL&UIUC | Report | ICIS report on Resilience | |||||||||
12 | 11: | 0030 | Vincent Baudoui | Total & ANL | Joint-Results | Round-off error and silent soft error propagation in exascale applications | Lyon_12_juin_2013_Error_propagation_in_exascale_applications_Vincent_Baudoui.pdf | |||||||
| 12:00 | Lunch |
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Numerical Algorithms | 13:30 | Bill Gropp | UIUC | Background | Topics for Collaboration in Numerical Libraries | |||||||||
14:00 | Paul Hoveland | ANL | BackgroundTBA | Argonne strategic plan in applied math |
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| 14:30 | Frederic NatafMarc Baboulin | INRIA&P6 | Background | Toward black-box adaptive domain decomposition methods | Using con dition numbers to assess numerical quality in high-performance computing applications | ||||||||
| 15:00 | Luke Olson | UIUC | Background | Opportunities in developing a more robust and scalable multigrid solver | |||||||||
15:30 | Break | |||||||||||||
| 16:00 | Marc Baboulin | Frederic Nataf | INRIA&P6INRIA | Background | Using condition numbers to assess numerical quality in high-performance computing applications | Toward black-box adaptive domain decomposition methods | |||||||
Resilience&fault tolerance and simulation Chair: Franck Cappello | 16:30 | Vincent Baudoui
| Bogdan Nicolae | IBMTotal & ANL | Joint -ResultsResult | Round-off error and silent soft error propagation in exascale applications | 17:00 | Bogdan Nicolae | IBM | Joint Result | AI-Ckpt: Leveraging Memory AI-Ckpt: Leveraging Memory Access Patterns for Adaptive Asynchronous Incremental Checkpointing | AICkpt-9thJLPC.pdf | ||
17:3000 | Martin Quison | INRIA | Result | Improving Simulations of MPI Applications Using A Hybrid Network Model with Topology and Contention Support | JLPC-simgrid-smpi.pdf | |||||||||
| 1817:0030 | Adjourn |
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| 1918:0045 | DinnerBus for Diner |
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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 | Update on MPI and OS/R Activities at Argonne | ||||||||
| 09:30 | Andra Ecaterina Hugo | INRIA | Results | TBA | Composing multiple StarPU applications over heterogeneous machines: a supervised approach | ||||||||
| 10:00 | Celso Mendes | UIUC | Background | TBA | Dynamic Load Balancing for Weather Models via AMPI | ||||||||
| 10:30 | Break |
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Big Data, I/O, Visualization | 11:00 | Dries Kimpe | ANL | ResultsTBA | Triton: Exascale Storage |
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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 | |||||||||
| 12:00 | Matthieu DorrierDorier | INRIA | Joint Result | Data Analysis of Ensemble Simulations: an In Situ Approach using Damaris | |||||||||
| 12:30 | Ian Foster | ANL | BackgroundTBA | Compiler optimization for distributed dynamic data flow programs |
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| 13:00 | Lunch |
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Mini Workshop1 Amphitheatre |
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Resilience | 14:00 | Ana Gainaru | UIUC | Results | Failure prediction on Challenges in predicting failures on the Blue Waters system. | |||||||||
| 14:30 | Xiang Ni | UIUC | Results | TBA | ACR: Automatic Checkpoint/Restart for Soft and Hard Error Protection. | ||||||||
| 15:00 | Tatiana Martsinkevich | INRIA & ANL | Result | TBA | On the feasibility of message logging in hybrid hierarchical FT protocols | ||||||||
| 15:30 | Mohamed Slim Bouguerra | INRIA & ANL | Result | TBA | Investigating the probability distribution of false negative failure alerts in HPC systems | ||||||||
| 16:00 | Break |
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| 16:30 | Amina Guermouche | UVSQ | Result | Multi-criteria Checkpointing Strategies: Response-time versus Resource Utilization | |||||||||
| 17:00 | Thomas Ropars | EPFL | Result | TBA | Towards efficient replication of HPC applications to deal with crash failures | Limited access | |||||||
| 17h30 | Mehdi Diouri | INRIA | Result | ECOFIT: A Framework to Estimate Energy Consumption of Fault Tolerance Protocols for HPC Applications | |||||||||
| 18:00 | Adjourn |
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Mini Workshop2 Room: Saint Maur |
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Numerical Algorithms and Libraries | 14:00 | Laura Grigori Jean Utke | INRIAANL | Result | TBA | Designing and implementing a tool-indedendent, adjoinable MPI wrapper library | ||||||||
| 14:30 | Stefan WildLaurent Hascoet | ANL INRIA | ResultTBA | The adjoint of MPI one-sided communications |
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| 15:00 | Frederic Hecht | Stefan Wild, | ANLINRIA/P6 | Result | TBA | Loud computations? Noise in iterative solvers | |||||||
| 15:30 | Jed Brown | ANL | Result | TBA | Vectorization, communication aggregation, and reuse in stochastic and temporal dimensions | ||||||||
| 16:00 | Break |
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| 16:30 | Yushan Wang | INRIA P11 | Result | TBA | Accelerating incompressible fluid flows simulations using SIMD or GPU computing | ||||||||
| 17:00 | Jean Utke | Frederic Hecht | INRIA/P6ANL | 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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FreeFem++, a user language to solve PDE. | ||||||||||||||
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| 18:00 | Adjourn |
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| 1918:0045 | BanquetBus for diner |
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| Lyon |
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Workshop Day 3 | Friday June 14th |
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Mini Workshop1 (cont.) Room: Les essarts |
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Resilience | 08:30 | Di Sheng | INRIA | Result | TBA | Optimization of Google Cloud Task Processing with Checkpoint-Restart Mechanism | ||||||||
| 09:00 | Guillaume Aupy | INRIA | Result | TBA |
| On the Combination of Silent Error Detection and Checkpointing | |||||||
Mini Workshop3 |
| 09:30 | DiscussionGuillaume Mercier | INRIA |
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| Result | Topology Management and MPI Implementations Improvements | ||||||
10:00 | Break |
| Mini Workshop3 | 10:30 | Guillaume Mercier | INRIA | ResultTBA | |||||||
Programming and Scheduling | 1110:0030 | Vincent Lanore | INRIA | Result | TBA | Static 2D FFT adaptation through a component model based on Charm++ | ||||||||
| 11:3000 | Anne Benoit | INRIA | Result | Energy-efficient scheduling | |||||||||
| 1211:0030 | François Tessier | INRIA | Result | TBA | Communication-aware load balancing with TreeMatch in Charm++ | ||||||||
| 12:3000 | DiscussionsClosing |
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| 1312:0030 | Closing and Lunch |
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Mini Workshop2 (cont.) Room: Saint Maur |
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Numerical Algorithms and Libraries | 08:30 | François Pellegrini | INRIA | Result | Shared memory parallel algorithms in Scotch 6 | |||||||||
| 09:00 | Luc GiraudAbdou Guermouche | INRIA | ResultTBA | Towards resilient parallel linear Krylov solvers |
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Mini Workshop4 | 09:30 | Kate Keahey | DiscussionsANL |
| Result | Research Topics and Collaboration Opportunities in the Nimbus Team |
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Clouds | 10:00 | Break |
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| Mini Workshop4 | |||||||
10:30 | Kate Keahey | Jonathan Rouzaud-Cornabas | CNRS&INRIAANL | Result | TBA |
| SimGrid Cloud Broker: Simulation of Public and Private Clouds | |||||||
Clouds | 11:00 | Gabriel AntoniuChristian Perez | INRIA | Result | TBA | On Component Models to Deploy Application on Clouds | ||||||||
| 11:30 | Christian PerezEddy Caron | INRIA | ResultTBA | Seed4C: Secured Embedded Element and Data privacy for Cloud Federation |
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| 12:00 | Eddy CaronClosing | INRIA | Result | TBA |
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| 12:30 | Discussions |
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| 13:00 | Closing and Lunch | Lunch |
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Abstracts
Paul Gibbon
Meeting the Exascale Challenge at the Juelich Supercomputing Centre.
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Energy-efficient scheduling
Jean Utke
Designing and implementing a tool-indedendent, adjoinable MPI wrapper library
The efficient computation of gradients by the "adjoint-mode" of algorithmic differentiation (AD) entails the inversion of MPI communication graphs. The logic to be implemented for adjoining non-blocking communication patterns is sufficiently complex to warrant a design of components that is independent of the algorithmic differentiation tool that provides the context in which the adjoint communication is to take place. We discuss (i) how we account for the different data models implied by the AD tool as well as the target language, (ii) the implementation choices among the possible adjoint communications, and (iii) the currently known limitations of our approach. We hope for feedback from the community regarding this design particularly with respect to performance and current developments in the MPI standard.
Laurent Hascoet
The adjoint of MPI one-sided communications
Computing gradients of numerical models by the adjoint mode of algorithmic differentiation is a crucial ingredient for model optimization, sensitivity analysis, and uncertainty quantification of many large-scale science and engineering applications. The adjoint mode implies a reversal of the data dependencies and consequently a reversal of communications in parallelized models. Building on previous studies regarding the adjoining of MPI two-sided communications, we investigate the construction of adjoints for certain one-sided MPI communications
Mehdi Diouri
ECOFIT: A Framework to Estimate Energy Consumption of Fault Tolerance Protocols for HPC Applications
In this talk, I will survey recent works on energy-efficient scheduling. The goal is to minimize the energy consumption of a schedule, given some performance constraints, for instance a bound on the total execution time. I will first revisit the greedy algorithm for independent tasks in this context. Then I will present problems accounting for the reliability of a schedule: if a failure may occur, then replication or checkpoint is used to achieve a reliable schedule. The goal remains the same, i.e., minimize the energy consumption under performance constraints
Jean Utke
Designing and implementing a tool-indedendent, adjoinable MPI wrapper library
The efficient computation of gradients by the "adjoint-mode" of algorithmic differentiation (AD) entails the inversion of MPI communication graphs. The logic to be implemented for adjoining non-blocking communication patterns is sufficiently complex to warrant a design of components that is independent of the algorithmic differentiation tool that provides the context in which the adjoint communication is to take place. We discuss (i) how we account for the different data models implied by the AD tool as well as the target language, (ii) the implementation choices among the possible adjoint communications, and (iii) the currently known limitations of our approach. We hope for feedback from the community regarding this design particularly with respect to performance and current developments in the MPI standard.
Laurent Hascoet
The adjoint of MPI one-sided communications
Computing gradients of numerical models by the adjoint mode of algorithmic differentiation is a crucial ingredient for model optimization, sensitivity analysis, and uncertainty quantification of many large-scale science and engineering applications. The adjoint mode implies a reversal of the data dependencies and consequently a reversal of communications in parallelized models. Building on previous studies regarding the adjoining of MPI two-sided communications, we investigate the construction of adjoints for certain one-sided MPI communications
Mehdi Diouri
ECOFIT: A Framework to Estimate Energy Consumption of Fault Tolerance Protocols for HPC Applications
Energy consumption and fault tolerance are two interrelated issues to address for designing future exascale systems. Fault tolerance protocols used for checkpointing have different energy consumption depending on parameters like application features, number of processes in the execution and platform characteristics. Currently, the only way to select a protocol for a given execution is to run the application and monitor the energy consumption of different fault tolerance protocols. This is needed for any Energy consumption and fault tolerance are two interrelated issues to address for designing future exascale systems. Fault tolerance protocols used for checkpointing have different energy consumption depending on parameters like application features, number of processes in the execution and platform characteristics. Currently, the only way to select a protocol for a given execution is to run the application and monitor the energy consumption of different fault tolerance protocols. This is needed for any variation of the execution setting. To avoid this time and energy consuming process, we propose an energy estimation framework. It relies on an energy calibration of the considered platform and a user description of the execution setting. We evaluate the accuracy of our estimations with real applications running on a real platform with energy consumption monitoring. Results show that our estimations are highly accurate and allow selecting the best fault tolerant protocol without pre-executing the application.
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Multigrid methods have increased in robustness in recent years due to new algorithmic advances and new theoretical developments. The result is a more robust multilevel framework leading to improved convergence for a wider range of non-elliptic problems. Yet, many of these developments have not been adapted at scale despite their intended use while many of the optimizations could be
strengthened by considering the high-perfromance computing architectures more directly. In this talk, we discuss a particular example of these recent optimizations in multigrid, to define optimal interpolation, that moves toward a more general framework, and highlight some focused directions for collaboration in this respect. In addition, recent trends in highthrouput computing have motivated algorithmic changes in the multigrid design. In this talk, we will also highlight some directions to futher advance multigrid solvers at scale based on this work with collaborion through the Joint Lab.leading to improved convergence for a wider range of non-elliptic problems. Yet, many of these developments have not been adapted at scale despite their intended use while many of the optimizations could be
strengthened by considering the high-perfromance computing architectures more directly. In this talk, we discuss a particular example of these recent optimizations in multigrid, to define optimal interpolation, that moves toward a more general framework, and highlight some focused directions for collaboration in this respect. In addition, recent trends in highthrouput computing have motivated algorithmic changes in the multigrid design. In this talk, we will also highlight some directions to futher advance multigrid solvers at scale based on this work with collaborion through the Joint Lab.
Paul Hovland
Argonne strategic plan in applied math
Jed Brown
Vectorization, communication aggregation, and reuse in stochastic and temporal dimensions
Transformative computing in science and engineering involves problems posed in more than just the spatial domain: temporal, stochastic, and parameter spaces also play a role. Current methods for solving such problems are predominantly based on the concept that the fundamental building block is the solution of a deterministic PDE model, or perhaps one time step of a transient model. This is practical: it permits comfortable partitioning of mathematical analysis and relatively unintrusive software interfaces, but it eagerly chooses which dimensions are treated sequentially, which are distributed in parallel, etc. These imposed choices leave developers of the PDE models banging their heads against the familiar challenges of efficiently utilizing increasingly precious memory bandwidth, hiding and reducing synchronization costs, and obtaining vectorization. Meanwhile, the stochastic and temporal dimensions provide structure that is ideally suited to extreme-scale architectures, if only they could be promoted to first-class citizens, alongside the spatial dimensions, in algorithmic analysis and in software. Exploiting this structure in ``full-space'' methods will require crosscutting development: improved convergence theory, efficient hardware-adapted algorithms, high-quality software libraries, and programming tools and run-time systems to facilitate the development of libraries and applications. In this talk, I present several examples and propose a guideline for reasoning about efficient mappings of full-space analysis onto parallel computers.
Celso Mende
Dynamic Load Balancing for Weather Models via AMPI
Load imbalances can severely limit the scalability of a parallel application. Typically, the solution adopted to overcome this problem is to change the application code as an attempt to distribute the load more uniformly across the available processors. This solution, however, requires deep knowledge of the application, and needs to be redone as new sources of imbalance arise. In this presentation, we show how an intelligent, adaptive runtime system can help in addressing this problem. Using Adaptime-MPI, an implementation of the MPI standard based on the Charm++ runtime system, we demonstrate how to achieve a better balance without requiring major changes or much knowledge about the application. As a case-study, we show an application of this approach with weather forecasting models, which can suffer from severe imbalances due to several sources, including dynamic variations in the atmosphere. Besides presenting recent results, we also point to some remaining challenges, which make opportunities for further work in this area.
Xiang Ni
ACR: Automatic Checkpoint/Restart for Soft and Hard Error Protection.
As the scale of machines increase, the HPC community has seen a steady decrease in reliability of the systems, and hence an increase in the down time. Moreover, soft errors such as bit flips do not prevent execution but generate incorrect results. Checkpoint/restart is by far the most commonly used fault tolerance method for hard errors, and its efficiency and scalability has been improved with recent research. In this talk, we will discuss a holistic methodology for automatically detecting and recovering from soft or hard faults with minimal application intervention. This is demonstrated by ACR: an automatic checkpoint/restart framework that performs application replication and automatically adapts the checkpoint period using online information about the current failure rate. ACR performs an application- and user-oblivious recovery. We empirically test ACR by injecting failures that follow different distributions for five applications and show low overhead when scaled to 131,072 cores. We also analyze the interaction between soft and hard errors and propose three recovery schemes that explore the trade-off between performance and reliability requirements.
Thomas Ropars,
Towards efficient replication of HPC applications to deal with crash failures
Ana Gainaru
Challenges in predicting failures on the Blue Waters system.
As the size of supercomputers increases, so does the probability of a single component failure within a time frame. With the growing operation cost of extreme scale supercomputers like Blue Waters, the act of predicting failures to prevent the loss of computation hours becomes cumbersome and presents a couple of challenges not encountered for smaller systems. The talk will focus on presenting online failure prediction and analyzing the Blue Water system. We show to what extent online failure prediction is a possibility at petascale and what are the challenges in achieving an effective fault prevention mechanism for Blue Waters.
Mohamed Slim Bouguerra
Investigating the probability distribution of false negative failure alerts in HPC systems
As large parallel systems increase in size and complexity, failures are inevitable and exhibit complex space and time dynamics. Several key results have demonstrated that recent advances in event log analysis can provide precise failure prediction. The state-of-the-art in failure prediction provides a ratio of correctly identified failures to the number of all predicted failures of over 90\% and its able to discover around 50\% of all failures in a system. However large part of failures are not predicted and considered as false negative alerts. Therefore, developing efficient fault tolerance strategies to tolerate failures requires a good perception and understanding of failure prediction properties and characteristics. In order to study and understand the properties and characteristics of the false negative alerts, we conduct in this paper a statistical analysis to discover the probability distribution of such alerts and their impact on fault tolerance techniques. To this end we study failures logs from different HPC production systems. We show that: (i) surprisingly the false negative distribution has the same nature as the failure distribution; (ii) after adding failure prediction we were able to infer statistical models that describes the inter arrival time between false negative alerts and so current fault tolerance can be applied on these systems; (iii) the current failures traces contain a high amount of correlation between the failure inter arrival time that can be used to improve the failure prediction mechanism. Another important result is that checkpoint intervals can still be computed from existing first order formula when failure distribution is purely random.
Rajeev Thakur,
Update on MPI and OS/R Activities at Argonne
This talk will give an update on MPI and OS/R activities at Argonne, including a big new project that is about to start in the area of exascale operating systems and runtime.
Andra Hugo
Composing multiple StarPU applications over heterogeneous machines: a supervised approach
Enabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great challenge. Even if a single runtime system is used underneath, scheduling tasks or threads coming from different libraries over the same set of hardware resources introduces many issues, such as resource oversubscription, undesirable cache flushes or memory bus contention. We present an extension of StarPU, a runtime system specifically designed for heterogeneous architectures, that allows multiple parallel codes to run concurrently with minimal interference. Such parallel codes run within scheduling contexts that provide confined execution environments which can be used to partition computing resources. Scheduling contexts can be dynamically resized to optimize the allocation of computing resources among concurrently running libraries. We introduce a hypervisor that automatically expands or shrinks contexts using feedback from the runtime system (e.g. resource utilization). We demonstrate the relevance of our approach using benchmarks invoking multiple high performance linear algebra kernels simultaneously on top of heterogeneous multicore machines. We show that our mechanism can dramatically improve the overall application run time (-34%), most notably by reducing the average cache miss ratio (-50%).
Vincent Lanore
Static 2D FFT adaptation through a component model based on Charm++
Adaptation algorithms for HPC applications can improve performance but their implementation is often costly in terms of development and maintenance. Component models such as Gluon++, which is built on top of Charm++, propose to separate the business code, encapsulated incomponents, and the application structure, expressed through a component assembly. Adaptation of component-based HPC applications can be achieved through the optimization of the assembly. We have studied such an approach with the adaptation to network topology and data size of a gluon++ 2D FFT application. In this talk, we present our work thus far and comment preliminary experimental results on the Grid'5000 platform.
Stefan Wild
Loud computations? Noise in iterative solvers
Roundoff errors, discretizations, numerical solutions to systems of equations, and adaptive techniques can destroy the smoothness of processes underlying computations at scale. Such computational noise complicates optimization, sensitivity analysis, and other applications that depend on the simulation output. We present a method for analyzing computational noise and illustrate the insights it enables on a collection of problems based on Krylov solvers.
Guillaume Aupy
On the Combination of Silent Error Detection and Checkpointing
In this talk, we revisit traditional checkpointing and rollback recovery strategies, with a focus on silent data corruption errors. Contrarily to fail-stop failures, such latent errors cannot be detected immediately, and a mechanism to detect them must be provided. We consider two models: (i) errors are detected after some delays following a probability distribution (typically, an Exponential distribution); (ii) errors are detected through some verification mechanism. In both cases, we compute the optimal period in order to minimize the waste, i.e., the fraction of time where nodes do not perform useful computations. In practice, only a fixed number of checkpoints can be kept in memory, and the first model may lead to an irrecoverable failure. In this case, we compute the minimum period required for an acceptable risk. For the second model, there is no risk of irrecoverable failure, owing to the verification mechanism, but the corresponding overhead is included in the waste. Finally, both models are instantiated using realistic scenarios and application/architecture parameters.
Dries Kimpe
Triton: Exascale Storage
In this talk, I will present a status update of our work on Triton, a newly designed exascale era storage system. In addition to Triton specific information, the presentation will also include a brief discussion about the tools and techniques that help us in implementing and designing Triton. One such tool is the use of discrete event simulation to quickly evaluate algorithms at scale before implementing them in Triton.
Tatiana Martsinkevich
On the feasibility of message logging in hybrid hierarchical FT protocols
abstract: Hybrid hierarchical fault tolerance protocols are a promising solution for providing fault tolerance on large scale. A hybrid hierarchical protocol combines coordinated checkpointing and message logging. A lot of work has been done on more efficient implementation of checkpointing protocols, however there are some questions that stay not fully studied with regards to message logging. Message logging requires some portion of the process memory and logged data is flushed to a safe storage together with the checkpoint. There are several possible strategies to take in case where there is not enough memory to log messages between two checkpoints. Each of the strategies will be discussed.
Di Sheng
Optimization of Google Cloud Task Processing with Checkpoint-Restart Mechanism
Programming multicore or manycore architectures is a hard challenge particularly if one wants to fully take advantage of their computing power. Moreover, a hierarchical topology implies that communication performance is heterogeneous and this characteristic should also be exploited. We developed two load balancers for Charm++ that take into account both aspects depending on the fact that the application is compute-bound or communication-bound. This work is based on our TreeMatch library that computes process placement in order to reduce an application communication cost based on the hardware topology. We show that the proposed load-balancing scheme manages to improve the execution times for the two classes of parallel applications.
Guillaume Mercier
Topology Management and MPI Implementations Improvements
Modern hardware architectures featuring multicores and a complex memory hierarchy raise challenges that need to be addressed by parallel applications programmers. It is therefore tempting to adapt an application communication pattern to the characteristics of the underlying hardware. The MPI standard features several functions that allow the ranks of MPI processes to be reordered according to a graph attached to a newly created communicator. In this talk, we explain how the MPI implementation of the MPI_Dist_graph_create function was modified to reorder the MPI process ranks to create a match between the application communication pattern and the hardware topology. The experimental results on a multicore cluster show that improvements can be achieved as long as the application communication pattern is expressed by a relevant metric. We also show several areas in MPI implementations where similar techniques can be beneficial.
Eddy Caron
Seed4C: Secured Embedded Element and Data privacy for Cloud Federation
In this talk we introduce the design of a secure federated cloud from end to end. We discussed the core of this platform based on a High Performance Computing middleware that uses federated clouds and other virtual resources as well classic HPC resources. We propose an architecture to ensure a high level of security from personal devices to the targeted virtual machine. The Seed4C platform improved security in each layer. With DIET Cloud, we are able to deploy a large-scale, distributed and secure HPC platform that spans across a large pool of resources aggregated from different providers through a secure way
Jonathan Rouzaud-Cornabas
SimGrid Cloud Broker: Simulation of Public and Private Clouds
Before migrating an application to public Clouds, it is required to evaluate its performance. Doing so on a real Cloud has a time and money cost. By using simulation, it is possible to evaluate the migration without money cost and with a reduced time cost. Furthermore, it ables to test different resource reservation and application allocation algorithms without paying for these resources. The same is true when using private and/or hybrid Clouds. SimGrid Cloud Broker (SGCB) ables to easily simulate a whole Cloud public and/or private to evaluate an application. Moreover, due to high modularity, SGCB can also be used to evaluate the inner working of a Cloud middleware. Accordingly, it is possible to evaluate the impact of new VM to PM placement algorithms and virtual machine image deployment policies. To conclude, SGCB is a general purpose simulator for evaluating applications that run on public and private Clouds and their compositions.
Frederic Hecht
FreeFem++, a user language to solve PDE.
I will make a small overview of the capability of FreeFem++. and I will focus on four computer science problems:
-the design of the language:
--- from store mathematical formulation (the weak form of PDE) to Data Structure (DS),
--- from DS to matrix and right hand side.
-the way to use lots of third party software : like : MUMPS, IPOPT, TETGEN, MKL, ....
-the use of mesh adapted
-the parallelization with MPI.
Ian Foster
Compiler optimization for distributed dynamic data flow programs
Distributed, dynamic data flow is an execution model well-suited for many large-scale parallel applications, particularly scientific simulations and analysis pipelines running on large, distributed-memory clusters. In this paper we describe compiler optimization techniques and an intermediate representation for distributed dynamic data flow programs. These techniques are applied to Swift/T, a high-level declarative language that allows flexible data flow composition of functions written in other programming languages such a C or Fortran. We show that compiler optimization can reduce communication overhead by 70-93% on distributed memory systems, making the high-level language competitive with hand-coded coordination logic for certain common application styles
Christian Perez
On Component Models to Deploy Application on Clouds
Clouds have become a complex ecosystem, providing many kinds of virtual machines (with different capabilities), of usage (on demand, spot instances, reservation), of data storage, etc. Moreover, some clouds provides worldwide "regions", enabling large scale distributed applications. Users also have very different requirements, potentially from execution to another such as minimizing execution time, respecting budget constraints, etc. Therefore, automatically and efficiently deciding how to map an application to a set of VM is a difficult challenge. This talk will discuss how the European PaaSage project as well as the French ANR MapReduce are using component models to describe and map an application structure, independently of anycloud, to an actual cloud
Kate Keahey
Research Topics and Collaboration Opportunities in the Nimbus Team
The advent of IaaS cloud computing promises acquisition and management of customized on-demand resources. What is the best way to leverage those resources? What new applications are emerging in this context? How will they change our work patterns? What new technical approaches need to be developed to support them? What new opportunities will they lead to? In this talk, I will describe tools the Nimbus team is developing, among others, in the context of the Ocean Observatory Initiative project, that focus on answering these questions. I will describe our approach and tools, the problems we are trying to address, as well as the interaction patterns associated with scientific applications currently driving our approach.
Abdou Guermouche
Towards resilient parallel linear Krylov solvers
The advent of exascale machines will require the use of parallel resources at an unprecedented scale, probably leading to a high rate of hardware faults. High Performance Computing (HPC) applications that aim at exploiting all these resources will thus need to be resilient, i.e., being able to still compute a correct solution even in presence of faults. In this work, we investigate possible remedies in the framework of the solution of large sparse linear systems that is often the inner most numerical kernel in many scientific and engineering applications and also one of the most time consuming part. More precisely, we present recovery followed by restarting strategies in the framework of Krylov subspace solvers where lost entries of the iterate are interpolated to define a new initial guess before restarting. In particular, we consider two interpolation policies that preserve key numerical properties of well-known solvers. We assess the impact of the recovery method, the fault rate and the number of processors on the robustness of the resulting linear solvers. We consider experiments with CG, GMRES and Bi-CGStab.