"Learned": Operating Systems

Audr¯unas Gruslys and Remi Munos and Ivo Danihelka and Marc Lanctot and Alex Graves. Memoryefficient backpropagation through time. In Proceedings of Conference on Neural Information Processing Systems (NeurIPS), 2016.

A. M. Caulfield, E. S. Chung, A. Putnam, H. Angepat, J. Fowers, M. Haselman, S. Heil, M. Humphrey, P. Kaur, J.-Y. Kim, D. Lo, T. Massengill, K. Ovtcharov, M. Papamichael, L.Woods, S. Lanka, D. Chiou, and D. Burger. A cloud-scale acceleration architecture. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2016.

V. Duddu, D. Samanta, D. V. Rao, and V. E. Balas. Stealing neural networks via timing side channels. arXiv preprint arXiv:1812.11720, 2018.

A. Eisenman, D. Gardner, I. AbdelRahman, J. Axboe, S. Dong, K. M. Hazelwood, C. Petersen, A. Cidon, and S. Katti. Reducing dram footprint with nvm in facebook. In Proceedings of the Thirteenth EuroSys Conference, 2018.

D. Firestone, A. Putnam, S. Mundkur, D. Chiou, A. Dabagh, M. Andrewartha, H. Angepat, V. Bhanu, A. M. Caulfield, E. S. Chung, H. K. Chandrappa, S. Chaturmohta, M. Humphrey, J. Lavier, N. Lam, F. Liu, K. Ovtcharov, J. Padhye, G. Popuri, S. Raindel, T. Sapre, M. Shaw, G. Silva, M. Sivakumar, N. Srivastava, A. Verma, Q. Zuhair, D. Bansal, D. Burger, K. Vaid, D. A. Maltz, and A. G. Greenberg. Azure accelerated networking: Smartnics in the public cloud. In 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18), 2018.

L. Guo, S. Zhai, Y. Qiao, and F. X. Lin. Transkernel: An executor for commodity kernels on peripheral cores. In 2019 USENIX Annual Technical Conference (USENIX ATC 19), 2019.

M. Hashemi, K. J. Swersky, J. A. Smith, G. Ayers, H. Litz, J. Chang, C. Kozyrakis, and P. Ranganathan. Learning memory access patterns. international conference on machine learning, pages 1919--1928, 2018.

N. P. Jouppi, C. Young, N. Patil, D. A. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. K. Bhatia, N. Boden, A. Borchers, R. Boyle, P. luc Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati,W. Gulland, R. Hagmann, C. R. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, D. Killebrew, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, M. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and D. H. Yoon. Indatacenter performance analysis of a tensor processing unit. In 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA), 2017.

A. Kalia, M. Kaminsky, and D. G. Andersen. Design guidelines for high performance rdma systems. In USENIX ATC '16 Proceedings of the 2016 USENIX Conference on Usenix Annual Technical Conference, 2016.

A. Khawaja, J. Landgraf, R. Prakash, M. Wei, E. Schkufza, and C. J. Rossbach. Sharing, protection, and compatibility for reconfigurable fabric with amorphos. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), 2018.

T. Kraska, M. Alizadeh, A. Beutel, E. H. hsin Chi, A. Kristo, G. Leclerc, S. Madden, H. Mao, and V. Nathan. Sagedb: A learned database system. In Proceedings of the 9th Biennial Conference on Innovative Data Systems Research (CIDR), 2019.

T. Kraska, A. Beutel, E. H. Chi, J. Dean, and N. Polyzotis. The case for learned index structures. In Proceedings of the 2018 International Conference on Management of Data, 2018.

A. Laga, J. Boukhobza, M. Koskas, and F. Singhoff. Lynx: a learning linux prefetching mechanism for ssd performance model. In 2016 5th Non-Volatile Memory Systems and Applications Symposium (NVMSA), 2016.

C. Lee, D. Sim, J. Y. Hwang, and S. Cho. F2fs: a new file system for flash storage. In FAST'15 Proceedings of the 13th USENIX Conference on File and Storage Technologies, 2015.

M. Mitzenmacher. A model for learned bloom filters and optimizing by sandwiching. neural information processing systems, pages 464--473, 2018.

A. Negi and K. Kishore. Applying machine learning techniques to improve linux process scheduling. In TENCON 2005 - 2005 IEEE Region 10 Conference, 2005.

Quoc Le and Barret Zoph. Using machine learning to explore neural network architecture. https://ai.googleblog. com/2017/05/using-machine-learning-to-explore.html, 2017.

C. J. Rossbach, Y. Yu, J. Currey, J.-P. Martin, and D. Fetterly. Dandelion: a compiler and runtime for heterogeneous systems. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, 2013.

W. Smith, I. T. Foster, and V. E. Taylor. Predicting application run times using historical information. job scheduling strategies for parallel processing, pages 122-- 142, 1998.

Q. Xu, H. Siyamwala, M. Ghosh, T. Suri, M. Awasthi, Z. Guz, A. Shayesteh, and V. Balakrishnan. Performance analysis of nvme ssds and their implication on real world databases. In Proceedings of the 8th ACM International