Jonathan Madsen earned his Ph.D. from Texas A&M University in Nuclear Engineering in December 2017 under the supervision of Dr. Ryan G. McClarren and Dr. John Ford and earned his M.S. in Nuclear Engineering (Health Physics) in August 2013 under the supervision of Dr. Gamal Akabani. His doctoral research focused on applying the concepts of compressed sensing to Monte Carlo scoring arrays in an effort to reduce memory allocation and reduce computation time through statistical de-noising during the reconstruction of the solution and his masters research focused on extending the classical Monte Carlo transport approach to utilize a quantum-mechanical description of the electron probability densities.
Jonathan is a member of the C++ standard committee, is a developer for the Kokkos programming model, and is leading an effort for the development of a modular API for extracting performance measurements and analysis called timemory. He has been a member of the Geant4 collaboration since 2011 and is currently serving as the Deputy Coordinator of the Run, Event, and Detector Responses Working Group and the Deputy Coordinator of the Geant4 Research and Development Task Force.
During his time as a NESAP for Data post-doc on the TomoPy project at NERSC, he implemented two high-fidelity iterative algorithms for tomographic reconstruction on the GPU, which were previously discarded due to inferiority to other algorithms on the CPU, and increased the scientific throughput by a factor exceeding 215x on 8 GPUs vs. the Edison supercomputer. The scientific impact of this result was a reduction of a minimum of ~6.5 hours to reach acceptable tolerance to ~110 seconds.
High-performance computing, parallel computing, multithreading, GPU-offloading, C, C++, Python, CUDA, Monte Carlo transport, deterministic transport, porting scientific software applications to highly-parallel machines, computational physics
- Madsen, J.R. et al. (2020) Timemory: Modular Performance Analysis for HPC. In: Sadayappan P., Chamberlain B., Juckeland G., Ltaief H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science, vol 12151. Springer, Cham
- Boehme, D, Huck, K A, Madsen, J, and Weidendorfer, J. Thu . "The Case for a Common Instrumentation Interface for HPC Codes". United States. https://www.osti.gov/servlets/purl/1574633.
- (2017). Disjoint Tally Method: A Monte Carlo Scoring Method Using Compressed Sensing to Reduce Statistical Noise and Memory. Doctoral dissertation, Texas A & M University. Available electronically from
- Madsen, J.R. and G. Akabani, Low-energy cross-section calculations of single molecules by electron impact : a classical Monte Carlo transport approach with quantum mechanical description. Phys Med Biol, 2014. 59(9): p. 2285-305.