A Quantitative Study of Locality in GPU Caches-Reference-Cited by-同舟云学术

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A Quantitative Study of Locality in GPU Caches

Published:2020 Issue: Volume: Page:228-242
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Lal Sohan^ORCID,Juurlink Ben

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-60939-9_16

Reference20 articles.

1. Al-Kiswany, S., Gharaibeh, A., Santos-Neto, E., Yuan, G., Ripeanu, M.: StoreGPU: exploiting graphics processing units to accelerate distributed storage systems. In: Proceedings of the 17th International Symposium on High Performance Distributed Computing, HPDC (2008)

2. Bakhoda, A., Yuan, G.L., Fung, W.W.L., Wong, H., Aamodt, T.M.: Analyzing CUDA workloads using a detailed GPU simulator. In: Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS (2009)

3. Lecture Notes in Computer Science;D Cederman,2012

4. Che, S., et al.: Rodinia: a benchmark suite for heterogeneous computing. In: Proceedings of the IEEE International Symposium on Workload Characterization, IISWC (2009)

5. Chen, X., Chang, L.W., Rodrigues, C.I., Lv, J., Wang, Z., Hwu, W.M.: Adaptive cache management for energy-efficient GPU computing. In: Proceedings of the 47th IEEE/ACM International Symposium on Microarchitecture, MICRO (2014)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Architecture-Aware Currying;2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT);2023-10-21

2. A Quantitative Study of Locality in GPU Caches for Memory-Divergent Workloads;International Journal of Parallel Programming;2022-04

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