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Performance and Algorithms Research
Performance and Algorithms Research

Nan Ding

Nan
Nan Ding
Research Scientist
Mobile: 510-598-8318
59-4022C

Nan Ding is a Research Scientist in the Performance and Algorithms group of the Computer Science Department at Lawrence Berkeley National Laboratory. Her research interests include high-performance computing, performance modeling, and performance optimization. Nan received her Ph.D. in computer science from Tsinghua University, Beijing, China, in 2018.

Journal Articles

Nan Ding, Pieter Maris, Hai Ah Nam, Taylor Groves, Muaaz Gul Awan, LeAnn Lindsey, Christopher Daley, Oguz Selvitopi, Leonid Oliker, Nicholas Wright, Samuel Williams, "Evaluating the potential of disaggregated memory systems for HPC applications", Concurrency and Computation, Practice and Experience (CCPE), May 2024, doi: https://doi.org/10.1002/cpe.8147

Nan Ding, Muaaz Awan, Samuel Williams, "Instruction Roofline: An insightful visual performance model for GPUs", CCPE, August 4, 2021, doi: 10.1002/cpe.6591

Nan Ding, Victor W. Lee, Wei Xue, Weimin Zheng, "APMT: an automatic hardware counter-based performance modeling tool for HPC applications", CCF Transactions on High Performance Computing, June 24, 2020,

Nan Ding, WeiXue, Zhenya Song, Haohuan Fub, Shiming Xu, WeiminZhenga, "An automatic performance model-based scheduling tool for coupled climate system models", JPDC, January 31, 2018,

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,

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,

Nan Ding, Muhammad Haseeb, Taylor Groves, Samuel Williams, "Evaluating the Performance of One-sided Communication on CPUs and GPUs", 2023 International Workshop on Performance, Portability & Productivity in HPC, November 12, 2023,

Taylor Groves, Chris Daley, Rahulkumar Gayatri, Hai Ah Nam, Nan Ding, Lenny Oliker, Nicholas J. Wright, Samuel Williams, "A Methodology for Evaluating Tightly-integrated and Disaggregated Accelerated Architectures", PMBS, November 2022,

Nan Ding, Samuel Williams, Hai Ah Nam, Taylor Groves, Muaaz Gul Awan, Christopher Delay, Oguz Selvitopi, Leonid Oliker, Nicholas Wright, "Methodology for Evaluating the Potential of Disaggregated Memory Systems", RESDIS, https://resdis.github.io/ws/2022/sc/, November 18, 2022,

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,

MG Awan, S Hofmeyr, R Egan, N Ding, A Buluc, J Deslippe, L Oliker, K Yelick, "Accelerating Large Scale de novo Metagenome Assembly Using GPUs", International Conference for High Performance Computing, Networking, Storage and Analysis, SC, January 1, 2021, doi: 10.1145/3458817.3476212

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,

A Zeni, G Guidi, M Ellis, N Ding, MD Santambrogio, S Hofmeyr, A Buluc, L Oliker, K Yelick, "LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment", Proceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium, IPDPS 2020, 2020, 462--471, doi: 10.1109/IPDPS47924.2020.00055

Nan Ding, Samuel Williams, "An Instruction Roofline Model for GPUs", Performance Modeling, Benchmarking, and Simulation (PMBS), BEST PAPER AWARD, November 18, 2019,

Haohuan Fu, Junfeng Liao, Nan Ding, Xiaohui Duan, Lin Gan,Yishuang Liang,Xinliang Wang,Jinzhe Yang,Yan Zheng,Weiguo Liu,Lanning Wang,Guangwen Yang, "Redesigning CAM-SE for peta-scale climate modeling performance and ultra-high resolution on Sunway TaihuLight (ACM Gordon Bell Prize Finalist)", SC'17, November 12, 2017,

Haohuan Fu, Junfeng Liao, Wei Xue, Lanning Wang, Dexun Chen, Long Gu, Jinxiu Xu, Nan Ding, Xinliang Wang, Conghui He, Shizhen Xu, Yishuang Liang, Jiarui Fang, Yuanchao Xu, Weijie Zheng, etc., "Refactoring and optimizing the community atmosphere model (CAM) on the sunway taihulight supercomputer", SC'16, November 13, 2016,

Nan Ding, Weu Xue, Xu Ji, Haoyu Xu, Zhenya Song, "CESMTuner: An Auto-Tuning Framework for the Community Earth System Model", HPCC'14, IEEE, August 20, 2014, doi: 10.1109/HPCC.2014.51

Book Chapters

Wei Xue, Xiaoge Xin, Jie Zhang, Wusheng Zhang, Haiping Wu, Zhenchun Huang, Tao Zhang, Huimin Li, Nan Ding, Huang Huang, "Development and Testing of a Multi-model Ensemble Coupling Framework", Book, (Springer, Singapore: January 1, 2016) Pages: 163-208

Presentation/Talks

Nan Ding, Muhammad Haseeb, Taylor Groves, Samuel Williams, Evaluating the Performance of One-sided Communication on CPUs and GPUs, 2023 International Workshop on Performance, Portability & Productivity in HPC, November 13, 2023,

Nan Ding, Samuel Williams, Hai Ah Nam, Taylor Groves, Muaaz Gul Awan, LeAnn Lindsey, Christopher Daley, Oguz Selvitopi, Leonid Oliker, Nicholas Wright, Methodology for Evaluating the Potential of Disaggregated Memory Systems, https://resdis.github.io/ws/2022/sc/, November 18, 2022,

Nan Ding, Samuel Williams, Yang Liu, Xiaoye S. Li, A Message-Driven, Multi-GPU Parallel Sparse Triangular Solver, July 19, 2021,

Nan Ding, Samuel Williams, An Instruction Roofline Model for GPUs, Performance Modeling, Benchmarking, and Simulation (PMBS), BEST PAPER AWARD, November 18, 2019,

Posters

Nan Ding, Samuel Williams, Sherry Li, Yang Liu, "Leveraging One-Sided Communication for Sparse Triangular Solvers", SciDAC19, July 18, 2019,

Samuel Williams, Charlene Yang, Khaled Ibrahim, Thorsten Kurth, Nan Ding, Jack Deslippe, Leonid Oliker, "Performance Analysis using the Roofline Model", SciDAC PI Meeting, July 2019,

Nan Ding, Victor W Lee, Wei Xue, Weimin Zheng, "Understanding Potential Performance Issues Using Resource-based Alongside Time Models", SC'18, November 13, 2018,

Last edited: 10/04/2023
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