サーバプラットフォームにおける並列プログラムの高電力効率実行
Notice bibliographique
Résumé
As the technology size shrinks, the amount of hardware resources equipped on a single server platform has been increased, which has contributed a significant improvement in the performance of server platform for a decade.However, the power consumption also continues to be increased, and modern server platforms are becoming heavily power-constrained.Since multithreaded programs are commonly executed on server platforms, it is recently a big challenge to improve their power efficiency.In order to improve the performance of multithreaded programs under power constraints, this dissertation first proposes two techniques: dynamic core and frequency scaling (DCFS) and dynamic thread mapping with frequency scaling (DTMFS).DCFS dynamically and heuristically controls the number of active cores and CPU frequency according to the scalability to the number of active cores of a running program.It is a totally dynamic approach because it does not require offline training.On the other hand, DTMFS dynamically adjusts the mapping of threads to cores and CPU frequency as parameters on multi-socket NUMA platforms.It determines the best setting of these parameters at runtime using artificial neural network-based models.These proposed techniques are implemented as user-level runtime systems and evaluated on a 64-core 8-node real NUMA platform under power constraints.Compared to executions with all available cores on the minimum CPU frequency, DCFS achieves a 33.0%improvement in performance on average across twelve benchmarks.Moreover, DTMFS outperforms a naive counterpart, which leaves thread mapping to a Linux scheduler and dynamically controls only the frequency, by up to 67.1% for twelve benchmarks.In addition, this dissertation focuses on breadth-first search (BFS) that is an important algo-rithm for graph analysis applications.Since such applications are used in various real services, it is essential to improve the power efficiency of BFS.As a state-of-the-art multithreaded BFS implementation is memory-intensive, its memory access pattern is investigated using a multicore simulator.The results reveal that the conventional address mapping schemes of modern memory controllers do not efficiently utilize row buffers in DRAM and wastefully use too many banks.On the basis of the observations, this dissertation proposes a new address mapping scheme per-row channel interleaving.It can significantly improve row buffer locality while sustaining bank parallelism and reduce the number of banks used at a time without hurting bank parallelism of each thread.Consequently, it improves the DRAM power efficiency by 30.3% relative to a conventional scheme on the simulator.In summary, this dissertation demonstrates that there are four important parameters to improve the power efficiency of multithreaded programs running on server platforms: CPU frequency, the number of active cores, the mapping of threads to cores, and address mapping schemes.As power problems on server platforms are expected to be more severe in the future, it is essential to carefully tune these parameters in order to realize high-performance and low-power computation.List of Tables 3.1 Specification of a 64-core platform. . . . . . . . . .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».