Παρακολούθηση
David Lo
David Lo
Google
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα alumni.stanford.edu - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
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
6942015
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
4062014
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
1852021
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
892012
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
862011
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
852016
Dynamic management of TurboMode in modern multi-core chips
D Lo, C Kozyrakis
2014 IEEE 20th International Symposium on High Performance Computer …, 2014
792014
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
472020
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
302019
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
262020
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
202021
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
82021
Autonomous warehouse-scale computers
S Dev, D Lo, L Cheng, P Ranganathan
2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020
82020
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
42022
The importance of generalizability in machine learning for systems
V Gohil, S Dev, G Upasani, D Lo, P Ranganathan, C Delimitrou
IEEE Computer Architecture Letters, 2024
32024
Reconciling High Efficiency with Low Latency in the Datacenter
D Lo
Stanford University, 2015
32015
Dynamic service level objective power control in distributed process
D Lo, L Cheng, RK Govindaraju
US Patent 9,436,258, 2016
22016
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
12022
Managing processing system efficiency
L Cheng, RK Govindaraju, H Zhu, D Lo, P Ranganathan, N Patil
US Patent 10,908,964, 2021
2021
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