Loren Schwiebert

Loren Schwiebert

Professor, Computer Science

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Loren Schwiebert

Biography

Department of Computer Science, Wayne State University

  • Professor, 2021 - Present
  • Department Chair, 2014 - 2022
  • Associate Professor, 2001 - 2021
  • Assistant Professor, 1995 - 2001

Education

Ph.D., Computer and Information Science, the Ohio State University (Advisor: D. N. Jayasimha), 1995
B.S., Computer Science (with a dual major in Mathematics), Heidelberg University (Tiffin, OH), 1986

Research Interests

My research group focuses on high-performance computing. In particular, we have been accelerating various scientific computing applications on GPUs and multicore CPUs. We also conduct basic research on algorithmic improvements for high-performance computing.

Note: Although I'm no longer doing research on the following topics, you can find more information about my prior research in wireless sensor networking and interconnection networks at my archived website.

As part of our research efforts, we contribute to the software development of two open-source software projects: GOMC and JETSCAPE.

GOMC

GOMC is open-source software for simulating molecular systems using the Metropolis Monte Carlo algorithm. The software has been written in object oriented C++, and uses OpenMP and CUDA to allow for efficient execution on multicore CPU and GPU architectures. GOMC employs widely-used simulation file types (PDB, PSF, CHARMM-style parameter files). GOMC can be used to study vapor–liquid equilibria, adsorption in porous materials, surfactant self-assembly, and condensed phase structure for complex molecules.

JETSCAPE

The Jet Energy-loss Tomography with a Statistically and Computationally Advanced Program Envelope (JETSCAPE) collaboration is an NSF funded multi-institutional effort that brings together experimental and theoretical physicists, statisticians, and computer scientists to design the next generation of event generators to simulate the physics of ultra-relativistic heavy-ion collisions. An open-source software framework is available to researchers to use existing simulation packages contributed by members of the collaboration as well as to develop and test their own models.

Publications

Select recent publications:

  1. Mohammad Soroush Barhaghi, Brad Crawford, Gregory Schwing, David Hardy, John E. Stone, Loren Schwiebert, Jeffrey Potoff, and Emad Tajkhorshid, “py-MCMD: Python Software for Performing Hybrid Monte Carlo – Molecular Dynamics Simulations with GOMC and NAMD,” J. Chem. Theory Comput., 19, 4983-4994 (2022). [Cover][Research Highlight Video]
  2. Q. He, M. Dong, and L. Schwiebert, “Octave Deep Compression: In-Parallel Pruning-Quantization on Different Frequencies,” IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), (2021). Best Paper Award.
  3. JETSCAPE Collaboration, "Phenomenological constraints on the transport properties of QCD matter with data-driven model averaging," Phy. Rev. Letter, vol. 126, pp. 242301, (2021) Editors' Suggestion.
  4. JETSCAPE Collaboration, "Multisystem Bayesian constraints on the transport coefficients of QCD matter," Phys. Rev. C, vol. 103, no. 5, pp. 054904, (2021).
  5. Younes Nejahi, Mohammad Soroush Barhaghi, Gregory Schwing, Loren Schwiebert, Jeffrey Potoff, Update 2.70 to “GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids,” SoftwareX 13, 100627, (2021).
  6. Y. Li and L. Schwiebert, “Memory-Optimized Wavefront Parallelism on GPUs,” International Journal of Parallel Programming, 48(6):1008-1031, (2020).
  7. Younes Nejahi, Mohammad Soroush Barhaghi, Jason Mick, Brock Jackman, Kamel Rushaidat, Yuanzhe Li, Loren Schwiebert, Jeffrey Potoff*, “GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids,” SoftwareX 9, 20-27 (2019). https://doi.org/10.1016/j.softx2018.11.005.
  8. Mohammad Soroush Barhaghi, Korosh Torabi, Younes Nejahi, Loren Schwiebert, and Jeffrey J. Potoff, “Molecular Exchange Monte Carlo. A generalized method for identity exchanges in grand canonical Monte Carlo simulations,” J. Chem. Phys. 149, 072318 (2018).
  9. Y. Li, L. Ghalami, L. Schwiebert, and D. Grosu, “A GPU Parallel Approximation Algorithm for Scheduling Parallel Identical Machines to Minimize Makespan,” IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 619–628, (2018).

 

Courses taught by Loren Schwiebert

Winter Term 2024 (current)

Fall Term 2023

Winter Term 2023

Winter Term 2022

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