The Beauty of Computer Science is

To Turn Challenges Into Opportunities


About Me

I am an Associate Professor of School of Computing at College of Engineering, Computing and Applied Sciences of Clemson University. Prior to joining this position, I was a Senior Research Scientist at Pacific Northwest National Laboratory. 

My research interests lie in high-performance computing (HPC), scientific computation and visualization, general-purpose computation on graphical processing units (GPGPU), big data, and machine learning, etc. In particular, I'm interested in the application of these technologies into power system domain problems, such as power system modeling and simulation, renewable energy integration, and advanced grid analytics, etc.

I am looking for 1-2 highly motivated Ph.D. students to join my research group. Both CPSC and ECE students with interests in HPC and GPGPU application development for power grid simulations are highly encouraged to contact me.



Computer science is a discipline that spans theory and application, which has a strong connection with all other disciplines.


Furthermore, when large-scale domain problems with high-throughput real-time dataset are concerned, high-performance computing (HPC) based parallel computation is extremely helpful in providing a fast and efficient technical solution.

My research goal is thus building a bridge between the fields of computer science and other disciplines, and applying effective and efficient HPC-based computations into pressing scientific and engineering problems, such as power grid simulation, computing biology modeling, and computer graphics acceleration, etc.

My current research interests are primarily focused on developing optimized HPC based parallel programming algorithms and architectures to speed up large-scale power system simulations to take advantage of the HPC resources for fast computation, and thus enabling real time or faster than real time computation with the implementation of Open Multi-Processing (OpenMP), Message Passing Interface (MPI), multithreading, and CUDA/OpenCL on shared-memory supercomputers, distributed-memory clusters, inter-connected workstations, or graphical processing units (GPU). 



Spring 2018

     CPSC8810/ECE8930: High-Performance Computing for Power System Modeling and Simulation

     Meeting Time: TU 2:00-4:45pm

     Meeting Room: McAdams 110E (Clemson), GEC 104 (CURI)

     Course Description: With the increasing demand, scale, and data information of power systems, fast real-time modeling and                                           simulation tools are becoming more important in power system operation and control. Improving the 

                                        computational efficiency of power system applications requires parallel computing implementation of

                                        the solution methods with high-performance-computing (HPC)) capabilities.

                                        This course is designed to provide instruction in the design and implementation of HPC technologies in

                                        power system modeling and simulation through extensive examples, studies, and project demonstrations                                         , so that the students can obtain hands-on parallel programming experiences to resolve large-scale real-

                                        world complex power grid problems.

                                        Multithreading, Open Multi-Processing (OpenMP) and Massive Programming Interface (MPI) will all be                                               introduced as the parallel programming tools, along with standard programming languages such as

                                        Matlab, C/C++, and Fortran, etc. Parallel solvers, existing power grid simulation tools, and parallel power                                         grid computational fram eworks will also be introduced. 

Fall 2017, Fall 2018

     CPSC/ECE4780/6780: General-Purpose Computation on Graphical Processing Units (GPGPU)

     Meeting Time: MW 1:00-2:15pm

     Meeting Room: McAdams 110E (Clemson), GEC 104 (CURI)

     Course Description: Graphics processing units (GPU) is a term introduced by NVIDIA in the late 1990s which typically handles                                           computation only for computer graphics. After 2001, with the advent of both programmable shaders and                                         floating-point support on graphics processors, general-purpose computing on GPUs became practical                                             and popular for scientific computing applications with its increasing speed and volume of computation.

                                        Nevertheless, extracting full performance from a GPU is challenging. Parallel algorithms are necessary but                                         far from sufficient. Careful layout of both control flow patterns and memory access patterns is required to                                         avoid flow divergence and bank conflicts, which can severely stall computational threads. Memory                                                     hierarchies, memory staging techniques, and the available synchronization primitives must be thoroughly                                         understood to provide tremendous performance improvements over conventional programming                                                       techniques on CPUs.

                                        This course is designed to provide instruction in the design and implementation of GPU-based solutions                                           to computationally intensive problems from a variety of disciplines. NVIDIA’s CUDA and OpenCL will both                                         be used as the programming language, and inter-operate with the open standard graphics language,                                               OpenGL, for massive data visualization. 




  • R Diao, Z Huang, Y Makarov, Y Chen, B Palmer, S Jin, J Wong, An HPC Based Realtime Path Rating Calculation Tool for Congestion Management with High Penetration of Renewable Energy, CSEE Journal of Power and Energy System, 3(4).


  • R Huang, S Jin, Y Chen, R Diao, B Palmer, Q Huang, and Z Huang, Faster Than Real-time Dynamic Simulation for Large-Size Power system with Detailed Dynamic Models Using High-Performance Computing Platform, Power & Energy Society General Meeting, 2017 IEEE, 1-5. 

  • Q Huang, R Huang, B Palmer, Y Liu, S Jin, R Diao, Y Chen, Y Zhang, A Reference Implementation of WECC Composite Load Model in Matlab and GridPACK, arXiv preprint arXiv:1708.00939.

  • S Jin, Z Huang, R Diao, D Wu, Y Chen, Comparative Implementation of High Performance Computing for Power System Dynamic Simulations, IEEE Transactions on Smart Grid 8 (3), 1387-1395.

  • R Diao, S Jin, F Howell, Z Huang, L Wang, D Wu, Y Chen, On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software, IEEE Transactions on Power Systems 32 (3), 2225-2233.


  • S Jin, Y Chen, R Diao, ZH Huang, W Perkins, B Palmer, Power Grid Simulation Applications Developed Using the GridPACK™ High Performance Computing Framework, Electric Power Systems Research 141, 22-30.

  • Y Chen, E Fitzhenry, S Jin, B Palmer, P Sharma, Z Huang, An Integrated Software Package to Enable Predictive Simulation Capabilities, Power Systems Computation Conference (PSCC), 2016, 1-6.

  • B Palmer, W Perkins, Y Chen, S Jin, D Callahan, K Glass, R Diao, M Rice, S Elbert, M Vallem, Z Huang, GridPACK™: A Framework for Developing Power Grid Simulations on High-Performance Computing Platforms, The International Journal of High Performance Computing Applications 30 (2), 223-240.


  • S Jin, Y Chen, D Wu, R Diao, Z Huang, Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments, IFAC-PapersOnLine 48 (30), 221-226.


  • B Palmer, W Perkins, Y Chen, S Jin, D Callahan, K Glass, R Diao, M Rice, S Elbert, M Vallem, Z Huang, GridPACK™: A Framework for Developing Power Grid Simulations on High-Performance Computing Platforms, Proceedings of the Fourth International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing, IEEE Press, 2014: 68-77.

  • Z Huang, R Diao, S Jin, Y Chen, Predictive Dynamic Security Assessment Through Advanced Computing, PES General Meeting| Conference & Exposition, 2014 IEEE, 1-5.

  • Z Huang, B Palmer, S Jin, G Bindewald, An Open-Source Approach to Accelerating Power System Dynamic Simulation, PES General Meeting| Conference & Exposition, 2014 IEEE, 1-1.

  • S Jin, D Chassin, Thread Group Multithreading: Accelerating the Computation of an Agent-Based Power System Modeling and Simulation Tool--GridLAB-D, System Sciences (HICSS), 2014 47th Hawaii International Conference on, 2536-2545.

  • P Wong, Z Huang, Y Chen, P Mackey, S Jin, Visual Analytics for Power Grid Contingency Analysis, IEEE computer graphics and applications 34 (1), 42-51


  • B Palmer, W Perkins, K Glass, Y Chen, S Jin, D Callahan, The GridPACK™ Toolkit for Developing Power Grid Simulations on High Performance Computing Platforms, Proceedings of the 3rd International Workshop on High Performance Computing, Networking and Analytics for the Power Grid, ACM, 2013: 3.

  • R Diao, Z Huang, C Jin, B Vyakaranam, S Jin, Y Makarov, Towards More Transmission Asset Utilization Through Real-time Path Rating, Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference, IEEE Press, 2013: 779-784.

  • S Jin, Z Huang, R Diao, D Wu, Y Chen, Parallel Implementation of Power System Dynamic Simulation, Power and Energy Society General Meeting (PES), 2013 IEEE, 1-5.

  • Y Chen, S Jin, M Rice, Z Huang, Parallel State Estimation Assessment with Practical Data, Power and Energy Society General Meeting (PES), 2013 IEEE, 1-5.


  • Z Huang, S Jin, R Diao, Predictive Dynamic Simulation for Large-scale Power Systems Through High-Performance Computing, High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion: 347-354.

  • S Jin, Y Chen, M Rice, Y Liu, I Gorton, A Testbed for Deploying Distributed State Estimation in Power Grid, Power and Energy Society General Meeting, 2012 IEEE, 1-7

  • Y Liu, W Jiang, S Jin, M Rice, Y Chen, Distributing Power Grid State Estimation on HPC Clusters - A System Architecture Prototype, Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International, IEEE Press, 2012: 1467-1476.

  • Y Chen, Z Huang, Y Liu, MJ Rice, S Jin, Computational Challenges for Power System Operation, System Science (HICSS), 2012 45th Hawaii International Conference on, 2141-2150.

  • Z Huang, N Zhou, Y Li, P Nichols, S Jin, R Diao, Y Chen, Dynamic Paradigm for Future Power Grid Operation, IFAC Proceedings Volumes 45 (21), 218-223.


  • R Diao, Z Huang, N Zhou, Y Chen, F Tuffner, J Fuller, S Jin, J Dagle, Deriving Optimal Operational Rules for Mitigating Inter-area Oscillations, Power Systems Conference and Exposition (PSCE), 2011 IEEE/PES, 1-8.


  • N Zhou, Z Huang, F Tuffner, J Pierre, S Jin, Automatic Implementation of Prony Analysis for Electromechanical Mode Identification from Phasor Measurements, Power and Energy Society General Meeting, 2010 IEEE, 1-8.

  • S Lu, Y Makarov, A Brothers, C McKinstry, S Jin, J Pease, Prediction of Power System Balancing Requirement and Tail
    Event, Transmission and Distribution Conference and Exposition, 2010 IEEE PES, 1-7.

  • S Jin, Z Huang, Y Chen, D Chavarría-Miranda, J Feo, P Wong, A Novel Application of Parallel Betweenness Centrality to Power Grid Contingency Analysis, Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium, IEEE Press, 2010: 1-7.


  • I Gorton, Z Huang, Y Chen, B Kalahar, S Jin, D Chavarría-Miranda, D Baxter, J Feo, A High-Performance Hybrid Computing Approach to Massive Contingency Analysis in the Power Grid, E-Science, 2009. E-Science'09. Fifth IEEE International Conference, 277-283.

  • Y Chen, S Jin, D Chavarría-Miranda, Z Huang, Application of Cray XMT for Power Grid Contingency Selection, Proceedings of Cray User Group, 4-7.

  • D Springer, J Miller, S Spinelli, L Pasa-Tolic, S Purvine, D Daly, R Zangar, S Jin, N Blumberg, C Francis, M Taubman, A Casey, S Wittlin, R Phipps, Platelet Proteome Changes Associated with Diabetes and During Platelet Storage for
    Transfusion, Journal of proteome research 8 (5), 2261-2272.


  • S Jin, A Suleiman, D Daly, D Springer, J Miller, Pathway Discovery by Genome-wide, High-throughput, Quantitative Mass Spectrometry, Genomic Signal Processing and Statistics, 2008. GENSiPS 2008. IEEE Press, 2008: 1-3.

  • J Miller, S Jin, W Morgan, A Yang, Y Wan, U Aypar, J Peters, D Springer, Profiling Mitochondrial Proteins in Radiation-induced Genome-unstable Cell Lines with Persistent Oxidative stress by Mass Spectrometry, Radiation research 169 (6), 700-706.


  • S Jin, D Daly, D Springer, J Miller, The Effects of Shared Peptides on Protein Quantitation in Label-free Proteomics by LC/MS/MS, Journal of proteome research 7 (01), 164-169.


  • J Miller, F Zheng, S Jin, L Opresko, H Wiley, H Resat, A Model of Cytokine Shedding Induced by Low Doses of Gamma Radiation, Radiation research 163 (3), 337-342.

  • S Jin, R Lewis, D West, A Comparison of Algorithms for Vertex Normal Computation, The Visual Computer 21 (1), 71-82.


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