Locality-Driven Dynamic GPU Cache Bypassing
This paper presents novel cache optimizations for massively parallel, throughput-oriented architectures like GPUs. Based on the reuse characteristics of GPU workloads, we propose a design that integrates such efficient locality filtering capability into the decoupled tag store of the existing L1 D-cache through simple and cost-effective hardware extensions.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1194296
- Report Number(s):
- PNNL-SA-109271; KJ0402000
- Resource Relation:
- Conference: Proceedings of the 29th ACM on International Conference on Supercomputing (ICS 2015), June 8-11, 2015, Newport Beach, California, 66-77
- Country of Publication:
- United States
- Language:
- English
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