Section: Other Grants and Activities
International Contracts and Projects
Explora'Doc, Lawrence Berkeley National Laboratory, USA
Thanks to this grant from the Rhône-Alpes region, and thanks to additional funding from Inria 's “explorateur” program, PhD student E. Agullo spent a 6-month period in the scientific computing group of the Lawrence Berkeley National Laboratory (California, USA). In Berkeley, he worked under the supervision of Xiaoye S. Li. The collaboration aims at comparing two direct out-of-core approaches (multifrontal and left-looking) for solving large sparse linear systems.
France-Berkeley Fund Award (project starts in 2008)
In the framework of the France-Berkeley Fund, we have been awarded a research grant to enable an exchange program involving both young and confirmed scientists. The collaboration will focus on massively parallel solvers for large sparse matrices and will reinforce the collaboration initiated by Emmanuel Agullo (see above). On the French side, this project also involves P. Amestoy (ENSEEIHT-IRIT), A. Guermouche (LaBRI) and I. Duff ( Cerfacs ).
Emmanuel Agullo and Jean-Yves L'Excellent participate to this project.
REDIMPS (Research and Development of International Matrix Prediction System) is a project funded by the Strategic Japanese-French Cooperative Program on "Information and Communications Technology including Computer Science" with the CNRS and the JST. The goal of this international collaboration is building an international sparse linear equation solver expert site. Among the objectives of the project, one resides in the cooperation of the TLSE partners and the JAEA in the testing, the validation and the promotion of the TLSE system that is currently released. JAEA, who is one of the leading institute and organization of Japanese HPC, is studying high-performance numerical simulation methods on novel supercomputers, and is expecting to find the best linear solver within this collaboration. By integrating knowledge and technology of JAEA and TLSE partners, it is expected that we will achieve the construction of an international expert system for sparse linear algebra on an international grid computing environment.
Yves Caniou, Eddy Caron, Frédéric Desprez and Jean-Yves L'Excellent participate to this project.
MIT-France Fund Award (2007)
Multicore architectures are now entering the mainstream, as we can now find them on simple laptops. This architectural change induces a pressing demand for new solutions to existing problems, like the efficient execution of streaming applications such as image, video, and digital signal processing applications. In this collaboration with S. Amarasinghe and B. Thies from the MIT CSAIL laboratory, we target the efficient scheduling and mapping of streaming applications on multicore architectures, especially on heterogeneous multicore architectures. This collaboration is an extension on our work on steady-state scheduling.
Matthieu Gallet and Frédéric Vivien participate to this project.
CNRS-USA grant SchedLife, University of Hawai`i (2007-2009)
We have been awarded a CNRS grant in the framework of the CNRS/USA funding scheme, which runs for three years starting in 2007. The collaboration is done with the Concurrency Research Group (CoRG) of Henri Casanova, and the Bioinformatics Laboratory (BiL) of Guylaine Poisson of the Information and Computer Sciences Department, of the University of Hawai`i at Manoa, USA.
The SchedLife project targets the efficient scheduling of large-scale scientific applications on clusters and Grids. To provide context for this research, we focus on applications from the domain of bioinformatics, in particular comparative genomics and metagenomics applications, which are of interest to a large user community today. So far, applications (in bioinformatics or other fields) that have been successfully deployed at a large scale fall under the “independent task model”: they consist of a large number of tasks that do not share data and that can be executed in any order. Furthermore, many of these application deployments rely on the fact that the application data for each task is “small”, meaning that the cost of sending data over the network can be ignored in the face of long computation time. However, both previous assumptions are not valid for all applications, and in fact many crucial applications, such as the aforementioned bioinformatics applications, require computationally dependent tasks sharing very large data sets.
In our previous collaborations, we have tackled the issue of non-negligible network communication overheads and have made significant contributions. For instance, we have designed strategies that rely on the notions of steady-state scheduling (i.e., attempting to maximize the number of tasks that complete per time unit, in the long run) and/or divisible load scheduling (i.e., approximate the discrete workload that consists of individual tasks as a continuous workload). These strategies provide powerful means for rethinking the deployment and the scheduling of independent task applications when network communication can be a bottleneck. However, the target applications in this project cannot benefit from these strategies directly and will require fundamental advances. This project aims to build upon and go beyond our past collaborations, with two main research thrusts:
Scheduling of applications with data requirements. We consider applications that require possibly multiple data files that need to be shared by multiple application tasks. These files may be extremely large (e.g., millions of genomic sequences) and may need to be updated frequently (e.g., when new sequences are identified). We must then ensure that file access is not a bottleneck.
Scheduling of multiple concurrent applications. We also plan to study the scheduling for multiple applications, i.e., launched by different (most likely competing) users. We then aim to orchestrate computation and communication in order to have the best aggregate performance. This is a difficult problem, first in order to define a good performance metric, and then to maximize this performance metric in a tractable way.
A. Benoit, E. Caron, F. Desprez, Y. Robert and F. Vivien participate to this project.
Associated-team MetagenoGrid (2008-2010)
This associated-team involves the exact same persons, and covers the same subject, as the CNRS-USA grant SchedLife described above.