Zheng-Ming Sheng is a distinguished professor of Shanghai Jiao Tong University and a fellow of the American Physical Society (APS). He has served as a member in the scientific advisory committee of the European project ELI and a committee member of the International Committee on Ultrahigh Intensity Lasers (ICUIL), an editorial board member of the journals Plasma and Fusion Research, Communication in Computational Physics, and Plasma Science and Technology. He has coauthored over 200 papers in refereed journals, which have been cited over 2000 times by other researchers. His current H-index is 30 and one of his papers was selected among the Selected Highly Cited Papers from 50 Years of Plasma Physics by Physics of Plasmas in 2008.
James Lin is the vice director at the Center for High-Performance Computing, Shanghai Jiao Tong University (HPC, SJTU). He has been working in HPC for more than 15 years and his current research interests include performance optimizations on emerging architectures. He has served as a TPC member for many HPC conferences, including the SC international conference for high-performance computing, International Parallel and Distributed Processing Symposium (IPDPS), and Symposium on Cluster Computing and the Grid (CCGrid). For the Intel® Parallel Computing Center (s) (Intel® PCC), he is in charge of teaching and organizing ITOC-International, the workshop aiming to bridge the communication gap between world HPC leaders and the HPC community in China.
Founded in 1896, Shanghai Jiao Tong University (SJTU) is the second oldest university in China. Intel® PCC at SJTU focuses on three major tasks: two code optimizations (VLPL-S and PILES), and teaching and promoting advancements from Intel and AL Technologies.
The particle-in-cell (PIC) simulation is well established kinetic simulations in plasma physics and astrophysics. PIC solves Maxwell equations for the electromagnetic fields and the equations of motion for macro-particles simultaneously. This kind of code has been proven to be very powerful for laser underdense plasma interactions in the hundreds of femtosecond and millimeter scale. The virtual laser plasma laboratory at SJTU (VLPL-S) has a modified in-house code that is used for both theoretical and experimental purposes. SJTU has successfully ported and optimized the code on Intel® Xeon Phi™ products. In this Intel® PCC, SJTU plans to scale VLPL-S to hundreds (or even more) of nodes, port them to Intel® Xeon Phi™ products, and add new physical models, such as the weighted particle technique and the quantum electrodynamics (QED) effects model.
Leukemia is in the top 10 of cancers in China; children account for 40% of the approximately 50,000 newly infected patients each year. Leukemia is a complex combination of many subtypes. Fortunately, some of most deadly subtypes can be cured. The experiences gained in leukemia research may shed light on other cancer research. SJTU has been developing the Precise Diagnoser of Leukemia Subtypes (PILES) code, a deep-learning-based, end-to-end precise diagnosis tool for leukemia subtypes, which includes three main components: the Gene Mutation Analyzer, Abnormal Cell Finder, and Leukemia Subtype Detector. In this Intel® PCC, SJTU plans to optimize PILES on Intel® architectures that include present and future Intel® Xeon Phi™ products.
SJTI also teaches HPC courses about high-performance computing technologies from Intel and AL Technologies. We organize the ITOC-International, the one-day workshop aiming to bridge the communication gap between world HPC leaders and the HPC community in China.
- May 10, 2018, Optimizing VLPL-S PIC on Intel® Xeon and Intel® Xeon Phi™, IPCC Asia Summit 2018
- May 11, 2018, Precise Leukemia Diagnosis via Deep Learning, IPCC Asia Summit 2018
- January 31, 2018, Real Test Cases Targeted Optimizations of VLPL-S Particle-in-cell Code on Intel Xeon and Intel Xeon Phi, IXPUG Workshop Asia 2018
- Feng He, Min Chen, Suming Weng, Zheng-Ming Sheng, October 17, 2017, Computational studies on laser plasma physics at LLP-SJTU, ITOC 2017
- Jianwen Wei, Ming Zhao, October 17, 2017, Cell-level Precise Diagnosis of Leukemia Subtypes via Deep Learning, ITOC 2017