Guggenheim Museum
NYC, 2018
My PhD research focuses on performance analysis for machine learning applications, including deep learning and Bayesian inference . Proper performance analysis can reveal system and architectural bottlenecks, provide essential information for choosing frameworks and platforms, and lead to performance optimizations.
For deep learning , I extracted the performance implications of key design features from deep learning frameworks (such as TensorFlow and Caffe2). For more details please refer to this paper. I also proposed a systematic analysis methodology that can reveal deeper insights that are difficult to discover for traditional approaches. For more details please refer to this paper. ParaDnn, a tool for this methodology, is available to use for other deep learning platforms.
Bayesian inference is an important branch of machine learning. However its computational characteristics are less studied in the community. I proposed BayesSuite to facilitate the research of such applications. Please refer to this paper for more details.
Sameer Kumar, Yu Emma Wang, Cliff Young, James Bradbury, Anselm Levskaya, Blake Hechtman, Dehao Chen, HyoukJoong Lee, Mehmet Deveci, Naveen Kumar, Pankaj Kanwar, Shibo Wang, Skye Wanderman-Milne, Steve Lacy, Tao Wang, Tayo Oguntebi, Yazhou Zu, Yuanzhong Xu, Andy Swing, "Exploring the limits of Concurrency in ML Training on Google TPUs." MLSys (2021).
Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim Hazelwood, David Brooks, "Exploiting Parallelism Opportunities with Deep Learning Frameworks." arXiv preprint arXiv:1908.04705 (2019).
Yu Emma Wang, Gu-Yeon Wei, David Brooks, "A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms." MLSys (2020).
(The arXiv version of the above paper) Yu Emma Wang, Gu-Yeon Wei, David Brooks, "Benchmarking TPU, GPU and CPU for Deep Learning." arXiv preprint arXiv:1907.10701 (2019).
Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, and David Brooks. "Demystifying Bayesian Inference Workloads." IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 177-189. IEEE, 2019.
Yu Emma Wang, Victor Lee, Gu-Yeon Wei, and David Brooks. "Predicting New Workload or CPU Performance by Analyzing Public Datasets." ACM Transactions on Architecture and Code Optimization (TACO). vol. 15, no. 4 (2019): 53:1–53:21.
Yu Emma Wang, Weikang Qian, Shuchang Zhang, Xiaoyao Liang, and Bo Yuan. "A Learning Algorithm for Bayesian Networks and Its Efficient Implementation on GPU," IEEE Transactions on Parallel and Distributed Systems. vol. 27, no. 1 (2016): 17–30.
Weichao Tang, Yu Emma Wang, Haopeng Liu, Tao Zhang, Chao Li, and Xiaoyao Liang. "Exploring Hardware Profile-Guided Green Datacenter Scheduling." International Conference on Parallel Processing (ICPP), pp. 11-20. 2015.
Yu Emma Wang. "Performance Analysis for Machine Learning Applications." PhD Dissertation, Harvard University, Nov 2019.
Feel free to download our software and use in your project. If you do, please cite our corresponding papers.
ParaDnn is a tool that enables systematic performance analysis for deep learning platforms.
Mille Crepe Bench is a multi-layer performance analysis tool for deep learning frameworks.
BayesSuite is a Bayesian inference benchmark suite based on Stan.
BN-GPU is a GPU implementation of a Bayesian network learning algorithm.
I enjoy food, traveling, photographing and interacting with people. These are samples of the outcome.
For more photos please refer to my 500PX page. Copyright reserved :)