Bull Sas

Commissariat &#xc0; L'&#xc9;nergie Atomique Et Aux &#xc9;nergies Alternatives

Centre National De La Recherche Scientifique

Université Montpellier 2 Sciences Et Techniques

Lionel Vincent

Suzanne Lesecq

Philippe Couvee

Edith Beigne

Salim Mimouni

Henri-Pierre Charles

Gaël Goret

Grégoire Pichon

Philippe Maurine

Trong-Ton Pham

Sophie Robert

Trong Ton Pham

Stephane Mancini

Mamady Nabe

Philippe COUVÉE

A method for detecting outlier behavior in a set of executions of one or several applications on an information processing device, implemented by a computer and comprising steps of

Method and apparatus for detecting outliers in a set of runs of software applications

The invention relates to a method for optimizing the execution parameters of a software application on an information processing platform, consisting in iteratively optimizing said parameters at each execution of said application, in which:

Automatic and self-optimized determination of execution parameters of a software application on an information processing platform

A method for improving the execution time of a computer application comprises at least one cycle includes: a step of determining the type of memory access time sequence occurring during execution of the computer application; a step of preloading data, from a file system to a cache memory system, according to the determined type of memory access time sequence. The determination step is carried out by a learning model having been previously configured using a database of certain predetermined types of memory access time sequences.

Method for improving the execution time of a computer application

A datum to be preloaded includes the acquisition of a, so-called 'model', statistical distribution of the deltas of a model access sequence, the construction of a, so-called 'observed', statistical distribution of the deltas of an observed access sequence, the identification in the observed statistical distribution, by comparing it with the model statistical distribution, of the most deficient class, that is to say of the class for which the difference NoDSM&#x2212;NoDSO is maximal, where NoDSM and NoDSO are the numbers of occurrences of this class that are deduced, respectively, from the model statistical distribution and from the observed statistical distribution, the provision as prediction of the datum to be preloaded into the cache memory, of at least one predicted address where the datum to be preloaded is contained, this predicted address being constructed on the basis of the most deficient class identified.

Method of predicting a datum to be preloaded into a cache memory

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Lionel Vincent