The HPCeSE Lab, directed by Dr. Shuangshuang Jin, is a research laboratory at Clemson University, dedicated to building a bridge between the fields of computer science and other disciplines by providing efficient high-performance computing (HPC)-based solutions to large-scale complex problems. ​

Current Projects


The current focus of research is to apply high-performance computing technologies in power and energy systems to enable fast or faster-than-real-time power system modeling and simulation, power electronics simulations, ground vehicle propulsion systems and smart energy, and advanced grid analytics, etc.
Ongoing Research Projects Include:

  • “Tool for Reliability Assessment of Critical Electronics in PV (TRACE- PV)”, Sponsored by DOE SETO, $1.6M, 2021-2025. Our task in this project is to develop an event-driven parallel computing-based simulator to accelerate switching-based sub-microsecond simulation.
  • "Virtual Prototyping of Autonomy-Enabled Ground Systems", Sponsored by DOD through GVSC, $11,028,645, 2020-2022. Our task in this project is to develop a holistic HPC framework for extensive modeling and simulation studies of energy delivery in electrical power architectures and power electronics for ground vehicle system.
  • "Learning to Adapt and Control for Complex Power Systems”, Sponsored by DOE AGM through PNNL, $208,994, 2020-2023. Our task in this project is to develop HPC algorithms to facilitate robust artificial-intelligence-based online controller parameter optimization and adaption for enhancing the resilience of power systems with increasing uncertainties and dynamics.
  • "Parallel Implementations of Power System Dynamic Simulation", Sponsored by Clemson University through Faculty Startups, $215,000, 2017-2021. Our task in this project is to develop fast dynamic simulation applications on a variety of parallel, distributed, or GPU computing architectures.
  • "Co-Simulaton of Accelerated Power System by High Performance Computing", 2021. Our task in this project is to develop an accelerated power system transmission and distribution systems co-simulation application using HELICS infrastructure on high-performance supercomputers.
Other Engaged Projects Include:
  • “CU-MRI: Power Device Analyzer”, Sponsored by Clemson University Major Research Instrumentation Program, $110,208, November 2020. The objective of this project is to request a power device analyzer system to support the research, grants, education, and next-generation workforce training at Clemson University.
  • “MRI: Acquisition of a Cyberinstrument for AI-Enabled Computational Science & Engineering; November 2019”, Sponsored by NSF, $651,000, September 2020. The objective of this project is to acquisit computational instrumentation that will directly drive fundamental research in artificial intelligence (AI) and enable the application of existing methodologies in AI to the advancement of several areas of science and engineering.

Past Projects

  • “An Integrated High-Performance Data-Driven Analytics Framework for Dynamic Security Assessment in Future Power Grid”, Sponsored by CU SUCCEEDS, $14,857.00, 2019-2020. The objective of this project is to developing an integrated high-performance data-driven analytics framework coupling advanced power grid modeling and simulation with state-of-the-art data-driven technologies, e.g., distributed and parallel computing, big data processing, machine learning and statistical analysis, and scientific visualization, to enable interactive online dynamic security assessment and decision support for the future power grid.
  • "CPS for the Power System Control and Operation Center of the Future”, Sponsored by CU SUCCEEDS, $7,500.00, 2019-2020. The objective of this project is to facilitate the development of the Digital Grid Innovation Laboratory, a state-of-the-art platform for the design, implementation and analysis of next-generation grid control centers.
  • “DAE Solver Interface Development For GridPACK Task Under DOE Advanced Grid Modeling”, Sponsored by DOE AGM through PNNL, $59,690.00, 2019. The objective of this project is to develop a DAE solver interface in GridPACK and to modify the dynamic simulation application to use this interface, which will enable users to use a variety of time integration algorithms in solving the dynamic simulation equations with reduced overall solution time.


We are a group of researchers passionate about speeding up scientific and engineering computational challenges through high-performance computing technologies, from distributed and parallel computing, GPGPU, to advanced data analytics and visualization.


  • -- "HPC is the trend for the future energy sector. We welcome everyone who's fansinated about HPC to join our team to shape the future with us together!" -- Dr. Shuangshuang jin
  • -- "I would like to be an HPC challenge handler." -- Cong Wang (PhD Student)
  • -- "I am passionate about optimizing real-world scientific and engineering problems with high efficiency and high quality." -- Liwei Wang (PhD Student)
  • -- "HPC, as a form of problem solving, is an intricate melding of software and hardware; in order to program well, you have to understand the underlying architecture well - and doing that is quite fun." -- Ray Crane (Former PhD Student)
  • -- "HPC is one of the most powerful tools to promote social development." -- Lei Zheng (MS Student)
  • -- "I see high performance computing as the ideal tool to answer the most challenging questions of science. From simulating the physics of molecular biology to predicting weather patterns, HPC has a place in almost every field of modern science." -- Cayden Wagner (MS Student)