Partners: INRIA GRAAL, INRIA GRAND-LARGE, CERFACS, CNRS, INRIA PARIS, LORIA
Petascale systems consisting of thousands to millions of resources have emerged. At the same, existing infrastructure are not capable of fully harnessing the computational power of such systems. The SPADES project will address several challenges in such large systems. First, the members are investigating methods for service discovery in volatile and dynamic platforms. Second, the members creating novel models of reliability in PetaScale systems. Third, the members will develop stochastic scheduling methods that leverage these models. This will be done with emphasis on applications with task dependencies structured as graph.
The overall objective of this project is to design and develop a cloud computing platform that enables the execution of complex services and applications over unreliable volunteered resources over the Internet. In terms of reliability, these resources are often unavailable 40\% of the time, and exhibit frequent churn (several times a day). In terms of "real, complex services and applications", we refer to large-scale service deployments, such as Amazon's EC2, the TeraGrid, and the EGEE, and also applications with complex dependencies among tasks. These commercial and scientific services and applications need guaranteed availability levels of 99.999\% for computational, network, and storage resources in order to have efficient and timely execution.
Partners:INRIA Nancy, INRIA Saclay, INRIA Bordeaux, University of Reims, IN2P3, University of Hawaii at Manoa
The goal of the USS-SimGrid project is to allow scalable and accurate simulations by means of the SimGrid simulation toolkit. This toolkit is widely used for simulation of HPC systems. We aim to extend the functionality of the toolkit to enable the simulation of heterogeneous systems with more than tens of thousands of nodes. There three main thrusts in this project. First, we will improve the models used in SimGrid, increasing their scalability and easing their instanciation. Second, we will develop tools that ease the analysis of detailed and large simulation results, and aid the management of simulation deployments. Third, we will improve the scalability of simulations using parallelization and optimization methods.
Partners: BULL SAS, CAPS entreprise, CEA CESTA, CEA INAC, INRIA RUNTIME, UVSQ PriSM
Processor architectures with many-core processors and special-purpose processors such as GPUS and the CELL processor have recenty emerged. These new and heterogeneous architectures require new applicaton programming methods and new programming models. The goal of the ProHMPT project is to address this challenge by focusing on the immense computing needs and requirements of real simulations for nanotechnologies. In order for nanosimulations to fully leverage heterogeneous computing architectures, project members will novel technologies at the compiler, runtime, and scientific kernely levels with proper abstractions and wide portability. This project brings experts from industry, in particular HPC hardware expertise from BULL and nanosimulation expertise from CEA.
Partners: RealTimeAtWork, Thales, ONERA, ENS CachanThe goal of this project to achieve performance guarantees for communicating embedded systems. Members will develop mathematical methods that give accurate bounds on maximum network delays in both space and aviation systems. The mathematical methods will be based on Network Calculus theory, which is type of queuing theory that deals with worst-case performance evaluation. The expected results will be novel models and software tools validated in mission-critical real-time embedded networks of the aerospace industry.
The increasing use of computerized systems in all aspects of our lives gives an increasing importance on the need for them to function correctly. The presence of such systems in safety-critical applications, coupled with their increasing complexity, makes indispensable their verification to see if they behaves as required . Thus the model checking which is the automated manner of formal verification techniques is of particular interest. Since verification techniques have become more efficient and more prevalent, it is natural to extend the range of models and specification formalisms to which model checking can be applied. Indeed the behavior of many real-life processes is inherently stochastic, thus the formalism has been extended to probabilistic model checking. Therefore, different formalisms in which the underlying system has been modeled by Markovian models have been proposed.
Stochastic model checkng can be performed by numerical or statistical methods. In model checking formalism, models are checked to see if the considered measures are guaranteed or not. We apply Stochastic Comparison technique for numerical stochastic model checking. The main advantage of this approach is the possibility to derive transient and steady-state bounding distributions as well as the possibility to avoid the state-space explosion problem. For the statistical model checking we study the application of perfect simulation by coupling in the past. This method has been shown to be efficient when the underlying system is monotonous for the exact steady-state distribution sampling. We consider to extend this approach for transient analysis and to model checking by means of bounding models and the stochastic monotonicity. As one of the most difficult problems for the model checking formalism, we also study the case when the state space is infinite. In some cases, it would be possible to consider bounding models defined in finite state space.
Members of MESCAL involved in this project are Jean-Marc Vincent and Bruno Gaujal.
The "ANR blanche" MEG, is composed of two teams: physicists working on electromagnetism from the LAAS (Toulouse) and the MESCAL project-team. The main objective is to study scaling properties in electromagnetism simulation applications and grids. The first results are promising. They demonstrate that the tools developed by Mescal on large data storage and middleware for deployment on clusters and grids are appropriate for that kind of application.
Yves Denneulin is the main members of MESCAL involved in this project.
MENRT-UJF-INPG, Rhône-Alpes Region, INRIA , ENS-Lyon have funded a cluster composed of 110 bi-processors Itanium2 connected with a Myrinet (donation of MyriCom) high performance network. This project is lead by MESCAL, MOAIS, ReMaP and SARDES. It is part of the CIMENT project which aims at building high performance distributed grids between several research labs.
DSLlab is a research project aiming at building and using an experimental platform about distributed systems running on DSL Internet. The objective is twofold:
Future generations of multiprocessors machines will rely on a NUMA architecture featuring multiple memory levels as well as nested computing units (multi-core chips, multi-threaded processors, multi-modules NUMA, etc.). To achieve most of the hardware's performance, parallel applications need powerful software to carefully distribute processes and data so as to limit non-local memory accesses. The ANR NUMASIS(NUMASIS: Adapting and Optimizing Applicative Performance on NUMA Architectures: Design and Implementation with Applications in Seismology) project aims at evaluating the functionalities provided by current operating systems and middleware in order to point out their limitations. It also aims at designing new methods and mechanisms for an efficient scheduling of processes and a clever data distribution on such platforms. These mechanisms will be implemented within operating systems and middleware. The target application domain is seismology, which is very representative of the needs of computer-intensive scientific applications.
Jean-François Méhaut, from MESCAL, is involved in this project.
After the success of the Grid'5000 project of the ACI Grid initiative led by the French ministry of research, INRIA is launching the ALADDIN project to further develop the Grid'5000 infrastructure and foster scientific research using the infrastructure. ALADDIN will build on Grid'5000's experience to provide an infrastructure enabling computer scientists to conduct experiments on large scale computing and produce scientific results that can be reproduced by others. ALADDIN focus on the following challenges :
Partners: INRIA ALGORILLE, INRIA GRAAL, INRIA MESCAL, TU Delft.
The MESCAL project-team participates in the ALEAE project of the INRIA ARC program. This project is led by Emmanuel Jeannot of the INRIA ALGORILLE project-team, who recently moved to the RUNTIME project-team. The project's goal is to provide models and algorithmic solutions in the field of resource management that cope with uncertainties in large-scale distributed systems. This work is based on the Grid Workloads Archive designed at TU Delft, Netherlands. Resulting from this collaboration, we have created the Failure Trace Archive, which is a repository of availabilty traces of distributed systems, and analytical tools. Moreover, we are conducting trace-driven experiments to test our solutions, to validate the proposed models, and to evaluate the algorithms. These experiments are being conducted using simulators and large-scale environments such as Grid'5000 in order to improve both models and algorithms.