Digiteo

As both the complexity of algorithms and architecture increase, development of scientific software becomes a challenge. In order to exploit future architecture, we consider a Multi-SPMD workflow programing model. Then, data transfer between tasks during computation highly depends on the architecture and middleware used. In this study we design an adaptive system for data management in a parallel programming environment which can express two level of parallelism. We show how the consideration of multiple strategies based on I/O and direct message passing can improve performances and fault tolerance in the YML-XMP environment. On a real application with a sufficiently large amount of local data, speedup of 1.36 for a mixed strategy to 1.73 for a direct message passing method are obtained compared to our original design.

Singular control for multidimensional Gaussian–Poisson processes with a long-run (or ergodic) and a discounted criteria is discussed. The corresponding Hamilton–Jacobi–Bellman equations are discussed. Complete details on the proofs and further extensions are left for future works.

We present in this paper an overview of optimization algorithms available in the Scilab software. We focus on the user's point of view, that is, we have to minimize or maximize an objective function and must find a solver suitable for the problem. The aim of this paper is to give a simple but accurate view of what problems can be solved by Scilab and what behavior can be expected for those solvers. For each solver, we analyze the type of problems that it can solve as well as its advantages and limitations. In order to compare the respective performances of the algorithms, we use the CUTEr library, which is available in Scilab. Numerical experiments are presented, which indicates that there is no cure-for-all solvers.