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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.

Setefan Wild

Stefan Wild

Loud computations? Noise in iterative solvers

 The adjoint of MPI one-sided communications
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 

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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

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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 PerezGille Fedak

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.