| Heracles: Improving resource efficiency at scale D Lo, L Cheng, R Govindaraju, P Ranganathan, C Kozyrakis Proceedings of the 42nd Annual International Symposium on Computer …, 2015 | 654 | 2015 |
| Towards energy proportionality for large-scale latency-critical workloads D Lo, L Cheng, R Govindaraju, LA Barroso, C Kozyrakis ACM SIGARCH Computer Architecture News 42 (3), 301-312, 2014 | 392 | 2014 |
| Sage: practical and scalable ML-driven performance debugging in microservices Y Gan, M Liang, S Dev, D Lo, C Delimitrou Proceedings of the 26th ACM International Conference on Architectural …, 2021 | 132 | 2021 |
| Rethinking DRAM power modes for energy proportionality KT Malladi, I Shaeffer, L Gopalakrishnan, D Lo, BC Lee, M Horowitz 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture, 131-142, 2012 | 89 | 2012 |
| Dynamic fine-grain scheduling of pipeline parallelism D Sanchez, D Lo, RM Yoo, J Sugerman, C Kozyrakis 2011 International Conference on Parallel Architectures and Compilation …, 2011 | 82 | 2011 |
| Improving resource efficiency at scale with Heracles D Lo, L Cheng, R Govindaraju, P Ranganathan, C Kozyrakis ACM Transactions on Computer Systems (TOCS) 34 (2), 6, 2016 | 79 | 2016 |
| Dynamic management of TurboMode in modern multi-core chips D Lo, C Kozyrakis 2014 IEEE 20th International Symposium on High Performance Computer …, 2014 | 77 | 2014 |
| Thunderbolt:{Throughput-Optimized},{Quality-of-Service-Aware} Power Capping at Scale S Li, X Wang, F Kalim, X Zhang, SA Jyothi, K Grover, V Kontorinis, ... 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2020 | 36 | 2020 |
| Kelp: Qos for accelerated machine learning systems H Zhu, D Lo, L Cheng, R Govindaraju, P Ranganathan, M Erez 2019 IEEE International Symposium on High Performance Computer Architecture …, 2019 | 29 | 2019 |
| Sage: Leveraging ml to diagnose unpredictable performance in cloud microservices Y Gan, M Liang, S Dev, D Lo, C Delimitrou arXiv preprint arXiv:2112.06263, 2021 | 21 | 2021 |
| Leveraging application classes to save power in highly-utilized data centers K Kaffes, D Sbirlea, Y Lin, D Lo, C Kozyrakis Proceedings of the 11th ACM Symposium on Cloud Computing, 134-149, 2020 | 20 | 2020 |
| Sage: Using unsupervised learning for scalable performance debugging in microservices Y Gan, M Liang, S Dev, D Lo, C Delimitrou arXiv preprint arXiv:2101.00267, 2021 | 6 | 2021 |
| Autonomous warehouse-scale computers S Dev, D Lo, L Cheng, P Ranganathan 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 6 | 2020 |
| Practical and Scalable ML-Driven Cloud Performance Debugging With Sage Y Gan, M Liang, S Dev, D Lo, C Delimitrou IEEE Micro 42 (4), 27-36, 2022 | 3 | 2022 |
| Reconciling High Efficiency with Low Latency in the Datacenter D Lo Stanford University, 2015 | 3 | 2015 |
| Dynamic service level objective power control in distributed process D Lo, L Cheng, RK Govindaraju US Patent 9,436,258, 2016 | 2 | 2016 |
| Enabling practical cloud performance debugging with unsupervised learning Y Gan, M Liang, S Dev, D Lo, C Delimitrou ACM SIGOPS Operating Systems Review 56 (1), 34-41, 2022 | 1 | 2022 |
| Managing processing system efficiency L Cheng, RK Govindaraju, H Zhu, D Lo, P Ranganathan, N Patil US Patent 10,908,964, 2021 | | 2021 |
Liqun ChengGoogle在 google.com 的电子邮件经过验证
