Data Oriented Simulation Framework in a Decentralized and Asymmetric HPC Ecosystem
Notice bibliographique
Résumé
The MOSAIC lightweight framework is a SAFRAN middleware targeting data management in an infrastructure including HPC facilities. In this paper we show how using a data oriented paradigm allows us to speed up simulation workflows and reduce the overall footprint of files, taking advantages of storage and computational resources availability within a large network of HPC infrastructure. Rather than defining a process consuming and producing data all along its run, we declare dependencies between existing and expected data and we let the system find the right production graph. This allows a makefile-like behavior of session following a workflow, trying to find actual data, fetching and using existing data matching the requirements or producing only those missing. The dependencies analysis is used to find potentially independent parts of the workflows to be run in parallel or to find which is the best workflow to select to obtain the expected final data. The dependency definition is formalized as a database query, this defines the set of accepted candidates as data source or product. Each data of the system has a set of qualifications, a query would look for existing data matching these qualifications and if found would eventually transfer it to the expected location for use. Definitions of data requirements and their relationships are specified in a profile. As these requirement are set of characteristics and not types, the users can define as many profiles they want. We have then different views of the same data, making it possible to have asymmetric understanding of a shared parts of large simulation processes. We have a lightweight representations of file contents, so-called datasets, which have qualifications describing key contents with predefined keywords. Some of these keywords can be generated automatically, for example when the file of the dataset is compliant to a standard like CGNS and it is easier to find key characteristics of the data. A Dataset refers to files but does not embed files, which means some production has been achieved, or no files, which means the dataset has files somewhere else on the network or is waiting for being produced. These datasets are duplicated in multiple databases across a network with multiple storage and computation resources. The framework is in charge of interpreting the data request all over the network and insure a lazy file transfer if required in a concurrency environment. On an HPC system this dramatically reduces the redundancy of files and the actual wall clock time of complex workflow executions. At the end of this paper, we illustrate the use and the performance of such a data oriented system on actual simulation workflows.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».