Cyril Briquet

Learn More
Scheduling Data-Intensive Bags of Tasks in P2P Grids leads to transfers of large input data files, which cause delays in completion times. We propose to combine several existing technologies and patterns to perform efficient data-aware scheduling: (1) use of the BitTorrent P2P file sharing protocol to transfer data, (2) data caching on computational(More)
Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for the closed-loop learning of map-pings from images to actions. This approach requires a family of function approximators that maps visual percepts to a real-valued function. For this(More)
Lattice-Boltzmann (LB) methods are a well-known technique in the context of computational fluid dynamics. By nature, they can easily be parallelized but their adaptation to the Grid environment is not trivial due to hardware heterogeneity (CPU, memory.. .) in a Grid. A load balancing method to dynamically handle the differences in terms of CPU number and(More)
Grid computing can be defined as coordinated resource sharing and problem solving in dynamic, multi-institutional collaborations [1]. As more Grids are deployed worldwide, the number of multi-institutional collaborations is rapidly growing. However, for Grid computing to realize its full potential, it is expected that Grid participants are able to use one(More)
In this paper, the deployment and execution of Iterative Stencil applications on a P2P Grid middleware are investigated. So-called Iterative Stencil applications are composed of sets of heavily-communicating, long-running Tasks. They thus require co-allocation of multiple reliable resources for extended periods of time. P2P Grids are totally decentralized(More)
P2P Grids are Grids organized into P2P networks where participant exchange computing time so as to complete computational tasks. Evaluating the performance of scheduling algorithms enables one to deploy those that are efficient. Performance is often evaluated experimentally or through simulation because these algorithms (typically heuristics) are too(More)
We introduce a compact hierarchical procedural model that combines feature-based primitives to describe complex terrains with varying level of detail. Our model is inspired by skeletal implicit surfaces and defines the terrain elevation function by using a construction tree. Leaves represent terrain features and they are generic parametrized skeletal(More)
  • 1