Bienvenue aux Mines Paristech
Bienvenue à MINES ParisTech
Newsletter International
Website
Théorie & Pratique
Vous êtes

webTV

Lecture

Séminaire PSL - Écosystèmes de médias I Session 4 Partie 4

Lecture

Séminaire PSL - Écosystèmes de médias I Session 4 Partie 2

Lecture

Séminaire PSL - Écosystèmes de médias I Session 4 Partie 3

Lecture

Séminaire PSL - Écosystèmes de médias I Session 4 Partie 1

Lecture

Lancement de la Chaire DEEP unissant HUTCHINSON, l'ESPCI et MINES ParisTech

+ Toutes les vidéos

Partager

Performance Model for Automatic Program Optimizations

Performance Model for Automatic Program Optimizations

Performance Model for Automatic Program Optimizations

Proposition de thèse

Spécialité

Informatique temps réel, robotique et automatique - Fontainebleau

Ecole doctorale

SMI - Sciences des Métiers de l'Ingénieur

Directeur de thèse

ANCOURT Corinne

Unité de recherche

Mathématiques et Systèmes

Contact
Date de validité

01/10/2018

Site Web
Mots-clés

compilation, parallel architecture, program optimization

compilation, parallel architecture, program optimization

Résumé

The objective of this thesis is to define performance models to guide automatic program optimizations for parallel machines coupling multi-core and GPUs.


The new parallel architectures being more efficient but more complex, program optimization becomes more and more laborious and expensive.

It is impossible to define a single program transformation recipe that would generate an optimal program, even for a single architecture. Each pair of application and architecture involves its own set of transformations to be applied to get closer to the optimal execution time of the program on a particular machine. In addition the set of possible transformation recipes is too large to be explored by the programmer, automatic tools are needed.

To guide the choice of transformations to be applied, application criteria and optimization cost functions must be defined. Because we cannot always measure those criteria, we use models. They must be established precisely in order to guide efficient decision-making and potentially serve as information for machine learning algorithms to define the optimization orchestration.

The aim of this thesis is to build performance models for the applications, as complete as possible, to help their mapping and optimization onto parallel architectures. Static and dynamic analyses could be used to collect some information such as the number of floating point operations, the number of memory operations, the bounds and depth of loops, the dependencies,.. Other parameters such as the estimation of cache miss and the memory fingerprint of loops have to be developed and refined in a parallel architectural context.

Based on the previous information, a methodology will be proposed to assist in the selection of appropriate program transformations that will improve chosen cost functions (execution time, memory footprint,..).

The objective of this thesis is to define performance models to guide automatic program optimizations for parallel machines coupling multi-core and GPUs.


The new parallel architectures being more efficient but more complex, program optimization becomes more and more laborious and expensive.

It is impossible to define a single program transformation recipe that would generate an optimal program, even for a single architecture. Each pair of application and architecture involves its own set of transformations to be applied to get closer to the optimal execution time of the program on a particular machine. In addition the set of possible transformation recipes is too large to be explored by the programmer, automatic tools are needed.

To guide the choice of transformations to be applied, application criteria and optimization cost functions must be defined. Because we cannot always measure those criteria, we use models. They must be established precisely in order to guide efficient decision-making and potentially serve as information for machine learning algorithms to define the optimization orchestration.

The aim of this thesis is to build performance models for the applications, as complete as possible, to help their mapping and optimization onto parallel architectures. Static and dynamic analyses could be used to collect some information such as the number of floating point operations, the number of memory operations, the bounds and depth of loops, the dependencies,.. Other parameters such as the estimation of cache miss and the memory fingerprint of loops have to be developed and refined in a parallel architectural context.

Based on the previous information, a methodology will be proposed to assist in the selection of appropriate program transformations that will improve chosen cost functions (execution time, memory footprint,..).

Contexte

Software research

Encadrement

Standard advising process

Profil candidat

This thesis requires a good knowledge and interest in compilation, computer architecture and code optimization.

Please send a CV, a motivation letter, the list of grades for your last academic year and 1 or 2 recommendation letters.

This thesis requires a good knowledge and interest in compilation, computer architecture and code optimization.

Please send a CV, a motivation letter, the list of grades for your last academic year and 1 or 2 recommendation letters.

Références

Please check with Corinne Ancourt, if interested.

Type financement

Concours pour un contrat doctoral

Retour à la liste des propositions

actualité

#PACTE : un nouveau statut pour l'entreprise

Formation #PACTE : un nouveau statut pour l'entreprise Votée le 9 octobre 2018, en première lecture à…
> En savoir +

Les nouveaux visages de MINES ParisTech

Formation Les nouveaux visages de MINES ParisTech Conformément aux nouvelles orientations de son Plan…
> En savoir +

Peut-on apprendre à sauver la planète ?

Formation Peut-on apprendre à sauver la planète ? Faut-il être un « super héros » pour changer le monde ? Il faut…
> En savoir +

Vladislav Yastrebov, Médaille de bronze CNRS 2018

Formation Vladislav Yastrebov, Médaille de bronze CNRS 2018 " La médaille de bronze récompense un premier travail…
> En savoir +

Plenesys, spin-off de l'École, Grand prix i-Lab 2018

Formation Plenesys, spin-off de l'École, Grand prix i-Lab… Belle reconnaissance pour la recherche conduite au sein du…
> En savoir +

+ Toutes les actualités

agenda

Du 7 septembre au 27 octobre 2018 Respiration minérale

Du 10 septembre au 27 novembre 2018 Mooc Conversion thermodynamique de la…

Du 18 septembre au 14 décembre 2018 Musique aux Mines 2018

Du 1 octobre 2018 au 31 janvier 2019 Mai 68 aux Mines

Du 22 octobre au 3 décembre 2018 Mooc Conversion thermodynamique de la…

+ Tous les événements

contact

Régine MOLINS
Direction de l'Enseignement
Service du Doctorat
> envoyer un mail

Plan du site
MINES
ParisTech

60, Boulevard Saint-Michel
75272 PARIS Cedex 06
Tél. : +33 1 40 51 90 00

Implantations
Formation
Mentions légales | efil.fr | ©2012 MINES ParisTech | +33 1 40 51 90 00 |