Section: Scientific Foundations
Parallel algorithms for heterogeneous platforms
Recently, a lot of work has been devoted to computational grids. Such computing architectures differ from usual parallel platforms in terms of heterogeneity (of both processing and communication resources) and scale (use of large distance network links with high latencies). Such platforms are usually not dedicated to one application, and therefore, security and fault tolerance problems also arise. In our works, we do not consider the security and fault tolerance problems, but rather concentrate on additional difficulties arising from the heterogeneity and the dynamicity (in terms of resource performances rather than topology) of such platforms.
Our goal is to design efficient scheduling algorithms for heterogeneous and non-dedicated platforms. Scheduling computational tasks or collective communications on a given set of processors is a key issue for high-performance computing. The traditional objective of scheduling algorithms is makespan minimization: given a task graph and a set of computing resources, find a mapping of the tasks onto the processors, and order the execution of the tasks so that (i) task precedence constraints are satisfied; (ii) resource constraints are satisfied; and (iii) a minimum schedule length is provided. However, makespan minimization turned out to be NP-complete problem in most practical situations and the advent of more heterogeneous architectural platforms is likely to even increase the computational complexity of the process of mapping applications to machines.
Many of the works presented in section 6.5 have been done in collaboration with the GRAAL project (INRIA Rhône-Alpes) during the PhD thesis of Loris Marchal (defended 10/06) which is co-directed by Olivier Beaumont and Yves Robert (GRAAL project).