Publikation: FusionCL: a machine-learning based appr...
Stammdaten
Titel: | FusionCL: a machine-learning based approach for OpenCL kernel fusion to increase system performance |
Untertitel: | |
Kurzfassung: | Employing general-purpose graphics processing units (GPGPU) with the help of OpenCL has resulted in greatly reducing the execution time of data-parallel applications by taking advantage of the massive available parallelism. However, when a small data size application is executed on GPU there is a wastage of GPU resources as the application cannot fully utilize GPU compute-cores. There is no mechanism to share a GPU between two kernels due to the lack of operating system support on GPU. In this paper, we propose the provision of a GPU sharing mechanism between two kernels that will lead to increasing GPU occupancy, and as a result, reduce execution time of a job pool. However, if a pair of the kernel is competing for the same set of resources (i.e., both applications are compute-intensive or memory-intensive), kernel fusion may also result in a significant increase in execution time of fused kernels. Therefore, it is pertinent to select an optimal pair of kernels for fusion that will result in significant speedup over their serial execution. This research presents FusionCL, a machine learning-based GPU sharing mechanism between a pair of OpenCL kernels. FusionCL identifies each pair of kernels (from the job pool), which are suitable candidates for fusion using a machine learning-based fusion suitability classifier. Thereafter, from all the candidates, it selects a pair of candidate kernels that will produce maximum speedup after fusion over their serial execution using a fusion speedup predictor. The experimental evaluation shows that the proposed kernel fusion mechanism reduces execution time by 2.83× when compared to a baseline scheduling scheme. When compared to state-of-the-art, the reduction in execution time is up to 8%. |
Schlagworte: | Scheduling, Kernel fusion, High-performance computing, Machine learning |
Publikationstyp: | Beitrag in Zeitschrift (Autorenschaft) |
Erscheinungsdatum: | 03.06.2021 (Online) |
Erschienen in: |
Computing
Computing
(
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Heftnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 1 - 32 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 03.06.2021 |
ISBN (e-book): | - |
eISSN: | 1436-5057 |
DOI: | http://dx.doi.org/10.1007/s00607-021-00958-2 |
Homepage: | https://link.springer.com/article/10.1007/s00607-021-00958-2 |
Open Access |
|
AutorInnen
Yasir Noman Khalid (extern) | ||||
Muhammad Aleem
|
||||
Usman Ahmed (extern) | ||||
Radu Aurel Prodan (intern) | ||||
Muhammad Arshad Islam (extern) | ||||
Muhammad Azhar Iqbal (extern) |
Zuordnung
Organisation | Adresse | ||||
---|---|---|---|---|---|
Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
|
AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
Sachgebiete | |
Forschungscluster | Kein Forschungscluster ausgewählt |
Zitationsindex |
Informationen zum Zitationsindex: Master Journal List
|
Peer Reviewed |
|
Publikationsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
|
Arbeitsgruppen |
|
Kooperationen
Organisation | Adresse | ||
---|---|---|---|
HITEC University
|
PK - 47080 Punjag |
||
National University of Computer and Emerging Sciences
|
PK Islamabad |
||
Western Norway University of Applied Sciences
|
NO - 5020 Bergen |
||
Southwest Jiaotong University, School of Computing and Artificial Intelligence
|
CN Chengdu |
Forschungsaktivitäten
(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
Projekte: |
|
Publikationen: | Keine verknüpften Publikationen vorhanden |
Veranstaltungen: | Keine verknüpften Veranstaltung vorhanden |
Vorträge: | Keine verknüpften Vorträge vorhanden |