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Section: Overall Objectives
Introduction
The goal 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.
To reach this goal, MOAIS gathers experts in : algorithm design, scheduling, parallel programming (both low level and high level API), interactive applications. The research directions of the MOAIS team are focused on scheduling problems with a multi-criteria performance objective: precision, reactivity, resources consumption, reliability, ... The originality of the MOAIS approach is to use the application's adaptability to control its scheduling:
the scheduler computes a schedule that verifies those conditions on the available resources;
each resource behaves independently and performs the decision of the scheduler.
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:
Scheduling: To formalize and study the related scheduling problems, the critical points are: the modeling of an adaptive application; the formalization and the optimization of the multi-objective problems; the design of scalable scheduling algorithms. We are interested in classical combinatorial optimization methods (approximation algorithms, theoretical bounds and complexity analysis), and also in non-standard methods such as Game Theory.
Adaptive parallel and distributed algorithms: To design and analyze algorithms that may adapt their execution under the control of the scheduling, the critical point is that algorithms are either parallel or distributed; then, adaptation should be performed locally while ensuring the coherency of results.
Programming interfaces and tools for coordination and execution: To specify and implement interfaces that express coupling of components with various synchronization constraints, the critical point is to enable an efficient control of the coupling while ensuring coherency. We develop the Kaapi runtime software that manages the scheduling of multithreaded computations with billions of threads on a virtual architecture with an arbitrary number of resources; Kaapi supports node additions and resilience. Kaapi manages the fine grain scheduling of the computation part of the application. To enable parallel application execution and analysis. We develop runtime tools that support large scale and fault tolerant processes deployment (TakTuk), visualization of parallel executions on heterogeneous platforms (Triva), reproducible CPU load generation on many-cores machines (KRASH).
Interactivity: To improve interactivity, the critical point is scalability. The number of resources (including input and output devices) should be adapted without modification of the application. We develop the FlowVR middleware that enables to configure an application on a cluster with a fixed set of input and output resources. FlowVR manages the coarse grain scheduling of the whole application and the latency to produce outputs from the inputs.
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.