Liqun Cheng
Liqun Cheng is a distinguished engineer at Google, where he is a technical lead for performance, TCO, and efficiency of data centers. His interests range from architecture, distributed systems, energy proportional computing and machine learning. He is particularly interested in interactions across domains with a major focus on software-hardware co-design. He obtained his PhD from the University of Utah and BS degree from Shanghai Jiao Tong University. Prior to Google, Liqun was a performance architect at Intel.
Authored Publications
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Google
Searching for Fast Models on Datacenter Accelerators
Ruoming Pang
Andrew Li
Norm Jouppi
Conference on Computer Vision and Pattern Recognition (2021)
Autonomous Warehouse-Scale Computers
Proceedings of the 57th Annual Design Automation Conference 2020, Association for Computing Machinery, New York, NY United States
Kelp: QoS for Accelerators in Machine Learning Platforms
Haishan Zhu
Rama Govindaraju
Mattan Erez
International Symposium on High Performance Computer Architecture (2019)
WSMeter: A Fast, Accurate, and Low-Cost Performance Evaluation for Warehouse-Scale Computers
Jaewon Lee
Changkyu Kim
Rama Govindaraju
Jangwoo Kim
Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (2018) (to appear)
Improving Resource Efficiency at Scale with Heracles
Rama Govindaraju
Christos Kozyrakis
ACM Transactions on Computer Systems (TOCS), 34 (2016), 6:1-6:33
Heracles: Improving Resource Efficiency at Scale
Rama Govindaraju
Christos Kozyrakis
Proceedings of the 42th Annual International Symposium on Computer Architecture (2015)
Towards Energy Proportionality for Large-Scale Latency-Critical Workloads
Rama Govindaraju
Luiz André Barroso
Christos Kozyrakis
Proceedings of the 41th Annual International Symposium on Computer Architecture, ACM (2014)
