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Contention Tracking in GPU Last-Level Cache

Authors :
Barrera, Javier
Kosmidis, Leonidas
Tabani, Hamid
Abella, Jaume
Cazorla, Francisco J.
Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
Barcelona Supercomputing Center
Source :
2022 IEEE 40th International Conference on Computer Design (ICCD)
Publication Year :
2022
Publisher :
IEEE, 2022.

Abstract

The Last-level cache (LLC) is one of the main GPU’s shared resources that contributes to improve performance but also increases individual kernel’s performance variability. This is detrimental in scenarios in which some level of performance predictability is required. While predictability can be regained by deploying cache partitioning (isolation) mechanisms, isolation negatively affects performance efficiency. This work shows that not partitioning the LLC and providing the ability to track the contention that kernels generate on each other allows them to share LLC space, hence increasing efficiency, while the system designer obtains a clear view of how each kernel affects each other in the LLC so as to balance performance and predictability goals. In this line, we propose GPU demotion counters (GDC), a low-overhead hardware mechanism to track contention that kernels generate on each other in the shared LLC. This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB-C21 and IJC-2020- 045931-I funded by MCIN/AEI/ 10.13039/501100011033 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773).

Details

ISBN :
978-1-66546-186-3
ISBNs :
9781665461863
Database :
OpenAIRE
Journal :
2022 IEEE 40th International Conference on Computer Design (ICCD)
Accession number :
edsair.doi.dedup.....9b0e5c5061f135278812eb25434b949a
Full Text :
https://doi.org/10.1109/iccd56317.2022.00021