Applying machine learning to enhance the cache performance using reuse distance
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s12065-022-00730-1.pdf
Reference42 articles.
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2. Jimenez DA, Teran E (2017) Multi perspective reuse prediction. In: Proceedings of the 50th Annual IEEE/ACM international symposium on microarchitecture (MICRO-50), pp 436 –448. https://doi.org/10.1145/3123939.3123942
3. Teran E, Wang Z, Jimenez DA (2016) Perceptron learning for reuse prediction. In: 49thAnnual IEEE/ACM international symposium on microarchitecture (MICRO). https://doi.org/10.1109/micro.2016.7783705
4. Rai JK, Negi A, Wankar R, Nayak KD (2010) A ML based meta-scheduler for multi-core processors. Int J Adapt Resil Auton Syst 1(4):46–59. https://doi.org/10.4018/jaras.2010100104
5. Pekhimenko G, Huberty T, Cai R, Mutlu O, Gibbons PB, Kozuch MA, Mowry TC (2015) Exploiting compressed block size as an indicator of future reuse. In: 2015 IEEE 21st international symposium on high performance computer architecture (HPCA), pp 51–63
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