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John R. Tramm

Assistant Computational Scientist



John Tramm’s background is in the fields of computational science and nuclear engineering. Specifically, he works to improve neutron transport methods that drive massively parallel simulations of nuclear reactors using some of the world’s largest supercomputers. This work involves analyzing and optimizing existing simulation applications, as well as developing fundamentally new methods of nuclear reactor simulation that can more optimally utilize modern high performance computing architectures. The overall goal of his efforts is to improve the ability to simulate nuclear reactors so as to reduce design uncertainties, improve economics, and most importantly to allow us to rapidly pursue new and novel reactor designs.

Currently he is a postdoctoral appointee. His research spans several areas, and he works on an interdisciplinary team of computer scientists, nuclear engineers, and computer hardware engineers in an effort to move the field of reactor simulation forward.

Research Areas of Interest

  • High-performance computing (HPC) application development and optimization
  • Evaluating performance of nuclear reactor simulation algorithms on experimental computer hardware designs
  • Updating Monte Carlo reactor simulation applications (OpenMC) and miniapplications (XSBench, RSBench, and SimpleMOC-kernel) for use with next generation supercomputer architectures
  • Researching the Random Ray Method (TRRM) of neutron transport, and continuing to develop the massively parallel Advanced Random Ray Code (ARRC) that implements TRRM
  • Preparing ARRC for the Aurora exascale supercomputer
  • Developing acceleration techniques for TRRM to reduce the number of iterations required to converge a full core reactor problem


  • PhD, Computational Nuclear Science and Engineering. Massachusetts Institute of Technology. 2018.
  • MS, Computer Science. University of Chicago. 2012.
  • BS, Nuclear Engineering. University of Illinois at Urbana-Champaign. 2009.


  • Del Favero Prize for Most Innovative Thesis. Massachusetts Institute of Technology, Department of Nuclear Science & Engineering. 2018.
  • Outstanding Student Service Award. Massachusetts Institute of Technology, Department of Nuclear Science & Engineering. 2016.
  • Computer Science TA of the Year. University of Chicago, Department of Computer Science. 2014.


Journal Articles
  • Tramm, J., Smith, K, Forget, B., and Siegel, A. (2018). ARRC: A random ray neutron transport code for nuclear reactor simulation. Annals of Nuclear Energy, 112, 693-714.
  • Tramm, J., Smith, K, Forget, B., and Siegel, A. (2017). The Random Ray Method for neutral particle transport. Journal of Computational Physics, 342, 229252.
  • Wallace, S., Zhou, Z., Vishwanath, V., Coghlan, S., Tramm, J., Lan, Z., and Papka, M. E. (2016). Application power profiling on IBM Blue Gene/Q. Parallel Computing, 57, 7386.
  • Tramm, J., Gunow, G., He, T., Smith, K., Forget, B., and Siegel, A. (2015). A task-based parallelism and vectorized approach to 3D Method of Characteristics (MOC) reactor simulation for high performance computing architectures. Computer Physics Communications, 202, 141150.
  • Dun, N., Fujita, H., Tramm, J., Chien, A., and Siegel, A. (2015). Data decomposition in Monte Carlo neutron transport simulations using global view arrays. International Journal of High Performance Computing Applications, 29 (3), 348 - 365.
  • Tramm, J., and Siegel, A. (2014). Memory bottlenecks and memory contention in multi-core Monte Carlo transport codes. Annals of Nuclear Energy, 82, 195202.
Conference Papers
  • Tramm, J., Smith, K., Forget, B. (2018). Early experience in full core reactor simulation with the Random Ray Method. PHYSOR 2018 – Reactor Physics paving the way towards more efficient systems.
  • Gunow, G., Tramm, J., Forget, B., Smith, K., and He, T. (2015). SimpleMOC – A performance abstraction for 3D MOC. ANS & MC 2015 – Joint International Conference on Mathematics and Computation (M&C), Supercomputing in Nuclear Applications (SNA), and the Monte Carlo (MC) Method.
  • Tramm, J., Siegel, A., Islam, T., and Schulz, M. (2014). XSBench - The development and verification of a performance abstraction for Monte Carlo reactor analysis. PHYSOR 2014 – The Role of Reactor Physics toward a Sustainable Future, Kyoto.
  • Tramm, J., Yoshii, K., and Siegel, A. (2014). Power profiling of a reduced data movement algorithm for neutron cross section data in Monte Carlo simulations. First International Workshop on Hardware-Software Co-Design for High Performance Computing (Co-HPC), In conjunction with SC14 and in collaboration with ACM SIGHPC, 2014.
  • Tramm, J., Siegel, A., Forget, B., and Josey, C. (2014). Performance analysis of a reduced data movement algorithm for neutron cross section data in Monte Carlo simulations. The Exascale Applications and Software Conference (EASC), Stockholm, 2014.