时间:2019-06-03 11:29:57

目:ThunderML: Machine Learning Systems on Heterogeneous Architectures

报告人:何丙胜 新加坡国立大学




The recent success of machine learning is not only due to more effective algorithms, but also more efficient systems and implementations. We have initiated a project called ThunderML, which aims at offering high performance machine learning as a service to users. So far, we have developed two systems for ThunderML: ThunderSVM and ThunderGBM, both of which exploit Graphics Processing Units (GPUs) and are open source. ThunderSVM supports all the functionalities of LibSVM (including classification, regression, and distribution estimation), and is often 100 times faster than LibSVM. ThunderGBM is for fast Gradient Boosting Decision Tree (GBDTs) and random forests, and is often 10 times faster than XGBoost and LightGBM. Both of them are open-sourced in GitHub and we welcome you to contribute. In this talk, I will present the background knowledge, key techniques and experimental results of ThunderSVM and ThunderGBM.

More information about ThunderML can be found at https://github.com/Xtra-Computing/. More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/.


Dr. Bingsheng He is currently an Associate Professor at Department of Computer Science, National University of Singapore. Before that, he was a faculty member in Nanyang Technological University, Singapore (2010-2016), and held a research position in the System Research group of Microsoft Research Asia (2008-2010), where his major research was building high performance cloud computing systems for Microsoft. He got the Bachelor degree in Shanghai Jiao Tong University (1999-2003), and the Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). His current research interests include cloud computing, database systems and high performance computing. His papers are published in prestigious international journals (such as ACM TODS and IEEE TKDE/TPDS/TC) and proceedings (such as ACM SIGMOD, VLDB/PVLDB, ACM/IEEE SuperComputing, ACM HPDC, and ACM SoCC). He has been awarded with the IBM Ph.D. fellowship (2007-2008) and NVIDIA Academic Partnership (2010-2011). Since 2010, he has (co-)chaired a number of international conferences and workshops, including IEEE CloudCom 2014/2015, BigData Congress 2018 and ICDCS 2020. He has served in editor board of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS) and Springer Journal of Distributed and Parallel Databases (DAPD). He has got editorial excellence awards for his service in IEEE TCC and IEEE TPDS in 2019.