Team moais

Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Other Grants and Activities

Section: Overall Objectives


The objective of the MOAIS team-project is to develop the scientific and technological foundations for parallel programming that enable to achieve provable performances on distributed parallel architectures, from multi-processor systems on chips to computational grids and global computing platforms. Beyond the optimization of the application itself, the effective use of a larger number of resources is expected to enhance the performance. This encompasses large scale scientific interactive simulations (such as immersive virtual reality) that involve various resources: input (sensors, cameras, ...), computing units (processors, memory), output (videoprojectors, images wall) that play a prominent role in the development of high performance parallel computing.

The research directions of the MOAIS team are focused on the scheduling problem with a multi-criteria performance objective: precision, reactivity, resources comsuption, reliability, ... The originality of the MOAIS approach is to use the application's adaptability to enable its control by the scheduling. The critical points concern designing adaptive malleable algorithms and coupling the various components of the application to reach interactivity with performance guarantees.

The originality of the MOAIS approach is to use the application's adaptability to control its scheduling:

To enable the scheduler to drive the execution, the application is modeled by a macro data flow graph, a popular bridging model for parallel programming (BSP, Nesl, Earth, Jade, Cilk, Athapascan, Smarts, Satin, ...) and scheduling. A node represents the state transition of a given component; edges represent synchronizations between components. However, the application is malleable and this macro data flow is dynamic and recursive: depending on the available resources and/or the required precision, it may be unrolled to increase precision (e.g. zooming on parts of simulation) or enrolled to increase reactivity (e.g. respecting latency constraints). The decision of unrolling/enrolling is taken by the scheduler; the execution of this decision is performed by the application.

The MOAIS project-team is structured around four axis:

Often, computing platforms have a dynamic behavior. The dataflow model of computation directly enables to take into account addition of resources. To deal with resilience, we develop softwares that provide fault-tolerance to dataflow computations. We distinguish non-malicious faults from malicious intrusions. Our approach is based on a checkpoint of the dataflow with bounded and amortized overhead.

For those themes, the scientific methodology of MOAIS consists in:

MOAIS research is not only oriented towards theory but also focuses on applicative software and hardware platforms developed with external partners. Significant efforts are made to build, manage and maintain these platforms. We are involved with other teams in four main platforms:


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