Yang Liu (original) (raw)
Yang Liu
Research Scientist
Applied Mathematics & Computational Research Division
Phone: 510-486-5283
Lawrence Berkeley National Laboratory
1 Cyclotron Road
Office: 50A-2145 Mail Stop: 50A-3111
Berkeley,CA 94720
Yang Liu is a research scientist in the Scalable Solvers Group of the Computational Research Division at Lawrence Berkeley National Laboratory (LBL). He is currently working on scalable sparse direct solver development supported by the Exascale Computing Project. Before joining LBL, he was a postdoctoral fellow in the Radiation Laboratory at University of Michigan. For more information, please visit: https://liuyangzhuan.github.io
Research Interests
- Numerical Linear Algebra -- sparse and dense direct solvers, butterfly algebras, and randomized algorithms.
- Computational Electromagnetics -- fast iterative and direct integral equation solvers and their applications.
- High Performance Computing -- communication avoiding algorithms, heterogeneous computing.
- Autotuning and Machine Learning
- Computational Plasma and Fluid Dynamics
- Inverse problems
Education
- Ph.D., Electrical Engineering, University of Michigan, May 2015.
- M.S., Mathematics, University of Michigan, Nov. 2014.
- M.S., Electrical Engineering, University of Michigan, May 2013.
- B.S., Electrical Engineering, Shanghai Jiao Tong University, June, 2010.
Journal Articles
"Newly released capabilities in distributed-memory SuperLU sparse direct solver", ACM Transactions on Mathematical Software, November 19, 2022,
- Download File: 3577197.pdf (pdf: 1.1 MB)
M. Wang, Y. Liu, P. Ghysels, A. C. Yucel, "VoxImp: Impedance Extraction Simulator for Voxelized Structures", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, November 2, 2022, doi: 10.1109/TCAD.2022.3218768
Hengrui Luo, Younghyun Cho, James W. Demmel, Xiaoye S. Li, Yang Liu, "Hybrid models for mixed variables in Bayesian optimization", June 6, 2022,
H. Luo, J.W. Demmel, Y. Cho, X. S. Li, Y. Liu, "Non-smooth Bayesian optimization in tuning problems", arxiv-preprint, September 21, 2021,
Yang Liu, Xin Xing, Han Guo, Eric Michielssen, Pieter Ghysels, Xiaoye Sherry Li, "Butterfly factorization via randomized matrix-vector multiplications", SIAM J. Sci. Comput., March 9, 2021,
Y. Liu, W. Sid-Lakhdar, E. Rebrova, P. Ghysels, X. Sherry Li, "A parallel hierarchical blocked adaptive cross approximation algorithm", The International Journal of High Performance Computing Applications, January 1, 2019,
A. C. Yucel, W. Sheng, C. Zhou, Y. Liu, H. Bagci, E. Michielssen, "An FMM-FFT Accelerated SIE Simulator for Analyzing EM Wave Propagation in Mine Environments Loaded With Conductors", IEEE Journal on Multiscale and Multiphysics Computational Techniques, 2018, 3:3-15,
Y. Liu, A. C. Yucel, V. Lomakin, and E. Michielssen, "Graphics processing unit implementation of multilevel plane-wave time-domain algorithm", IEEE Antennas Wireless Propag. Lett., 2014,
A. C. Yucel, Y. Liu, H. Bagci, and E. Michielssen, "Statistical characterization of electromagnetic wave propagation in mine environments", IEEE Antennas Wireless Propag. Lett., 2013,
Conference Papers
Nan Ding, Brian Austin, Yang Liu, Neil Mehta, Steven Farrell, Johannes P. Blaschke, Leonid Oliker, Hai Ah Nam, Nicholas J. Wright, Samuel Williams, "A Workflow Roofline Model for End-to-End Workflow Performance Analysis", Supercomputing (SC), November 2024,
- Download File: Workflow_roofline-6.pdf (pdf: 1.2 MB)
Yang Liu, Nan Ding, Piyush Sao, Samuel Williams, Xiaoye Sherry Li, "Unified Communication Optimization Strategies for Sparse Triangular Solver on CPU and GPU Clusters", Supercomputing (SC), November 2023,
- Download File: SC23_3DSpTRSV_final.pdf (pdf: 2.9 MB)
Yang Liu, "A comparative study of butterfly-enhanced direct integral and differential equation solvers for high-frequency electromagnetic analysis involving inhomogeneous dielectrics", May 29, 2022,
- Download File: comparative_study-v2.pdf (pdf: 3.3 MB)
X. Zhu, Y. Liu, P. Ghysels, D. Bindal, X. S. Li, "GPTuneBand: multi-task and multi-fidelity Bayesian optimization for autotuning large-scale high performance computing applications", SIAM PP, February 23, 2022,
- Download File: GPTuneBand.pdf (pdf: 1.4 MB)
Y. Cho, J. W. Demmel, X. S. Li, Y. Liu, H. Luo, "Enhancing autotuning capability with a history database", IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), December 20, 2021,
- Download File: GPTuneHistoryDB.pdf (pdf: 390 KB)
Nan Ding, Yang Liu, Samuel Williams, Xiaoye S. Li, "A Message-Driven, Multi-GPU Parallel Sparse Triangular Solver", SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), July 19, 2021,
- Download File: Multi-GPU-SpTRSV-ACDA21-.pdf (pdf: 897 KB)
Y. Liu, W. M. Sid-Lakhdar, O. Marques, X. Zhu, C. Meng, J. W. Demmel, X. S. Li, "GPTune: multitask learning for autotuning exascale applications", PPoPP, February 17, 2021, doi: 10.1145/3437801.3441621
Nan Ding, Samuel Williams, Yang Liu, Xiaoye S. Li, "Leveraging One-Sided Communication for Sparse Triangular Solvers", 2020 SIAM Conference on Parallel Processing for Scientific Computing, February 14, 2020,
- Download File: One-side-SPTRS-SIAM-PP20-.pdf (pdf: 2.9 MB)