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NESAP Postdocs

Since introducing the NERSC Exascale Scientific Applications Program (NESAP) in 2015, NERSC has hired a continuous contingent of postdoctoral fellows and placed them with NESAP code optimization teams. The postdocs are working in multidisciplinary teams composed of computer scientists, applied mathematicians, domain scientists and performance optimization experts that are helping NERSC users transition codes to the Cori system.

Current NESAP Postdocs
Name Hire Date Ph.D. Institution Ph.D. Discipline Project / Contributions
Soham Ghosh 2020 Florida State University Physics Developing and accelerating NESAP for simulating massively parallel software for computing electronic structures within many-body perturbation theory. The work involves porting and scaling both screening and correlation methods on the GPU as well as making the routines more portable between different accelerating hardware.
Dhruva Kulkarni 2020 Clemson University Physics Developing and accelerating NESAP for Simulations application WDMApp. Evaluating and optimizing the performance of various kernels and solvers in WDMApp across different hardware (NVIDIA, AMD, Intel) and software (Kokkos, OpenMP acceleration) stacks.
Nestor Demeure 2021 University Paris Saclay CS and Applied Math Adding GPU capabilities to the TOAST Astrophysics application stack which is used to study the Cosmic Microwave Background. This implied updating the benchmarking infrastructure of the project. Adding support or GPU libraries including CuBLAS and CuFFT. Implementing and optimizing GPU kernels in both JAX and OpenMP target offload in order to compare both technologies.
Felix Wittmer 2021 Zurich U. Physics Acceleration of the EXAFEL (LCLS) application stack. The CCTBX project currently relies on Nvidia/CUDA to achieve GPU acceleration. To be more vendor independent, the project involves porting the nanoBragg code from CCTBX to Kokkos and evaluate the performance on Perlmutter, Frontier and Aurora.
Nick Tyler 2021 U. of SC Physics Workflow and GPU optimization of JGI (genomics) pipelines
Shashank Subramanian 2021 UT Austin CS AI based acceleration of computational fluid dynamics and weather modeling NeuWCast.
Lipi Gupta 2021 U. of Chicago Physics Acceleration of beamline science workflows from the ALS to NERSC
Vinicius Mikuni 2021     Implementation and optimization of machine Learning algorithms in High Energy Physics.
Richard Barnes 2021 UC Berkeley Geophysics Applying “depression hierarchy” algorithms, learned indexes and AI-based algorithms (Deep neural networks) to important problems in geoscience, electric public transportation, connectomics and Genomics.
Past NESAP Postdocs
Name Term Ph.D. Institution Ph.D. Discipline After PostDoc Project / Contributions
Jihan Kim 2009 - 2013   Computational Physics Korea Advanced Institute of Science and Technology Developed a GPU code to accelerate screening of microporous materials to absorb carbon dioxide gas from power plant flue gases.
Robert Preissl 2010 - 2011   Computer Science IBM; Ticketfly; Kitty Hawk Worked with physicists at the Princeton Plasma Physics Laboratory (PPPL) to implement PGAS and hybrid programming solutions for highly scalable particle-in-cell codes
Xuefei (Rebecca) Yuan 2010 - 2012   Applied Mathematics Bank of America; Wells Fargo Improved hybrid linear software routines based on the Schur complement method in collaboratoin LBNL CRD.
Wangyi (Bobby) Liu 2010 - 2013   Applied Mathematics Google Improved the ALE-AMR code and developed scalable solutions for new physics models for Heavy-Ion science.
Brian Austin 2010 - 2011   Chemistry LBNL NERSC Developed simulations for next-generation light sources based on x-ray free electron lasers
Kjiersten Fagnan 2010 - 2012   Applied Mathematics LBNL NERSC; LBNL JGI Developed numerical simulations of carbon sequestration and porous media flow.
Filipe Maia 2010 - 2012   Physics (Molecular Biophysics) LBNL ALS; Uppsala Accelerated biological imaging codes and Earth Sciences Division and geophysical imaging codes with GPUs
Praveen Narayanan 2010 - 2012   Mechanical Engineering NVIDIA; Ford Completed performance characterization and benchmarking of several parallel applications running at NERSC, in collaboration with code teams at several DOE facilities.
Christos Kavouklis 2011- 2015   Engineering Mechanics LBNL CRD; LLNL Fast computation of volume potentials on structured grids using the method of local corrections.
Brian Friesen 2015 - 2016   Physics (Astro) LBNL NERSC Accelerated the Boxlib framework and representative apps for Cori including addition of Tiling, OpenMP threading, Vectorization and Burst Buffer support.
Taylor Barnes 2015 - 2017 CalTech Chemistry VA Tech (NSF MOLSSI) Optimization of the NESAP Quantum ESPRESSO application (Hybrid Functional excecution in particular) for the Cori system. Optimizations included OpenMP support, vectorization and adding multiple new levels of parallelism over electron orbitals.
Andrey Ovsyannikov 2015 - 2017   Fluid
Intel Performance optimization of the Chombo framework and chombo-crunch code for the Cori system at NERSC.
Tuomas Koskela 2016 - 2018   Physics University of Helsinki Accelerated the XGC1 NESAP code for Cori including vectorization support, communications acceleration and the development of the particle in cell (PIC) mini-app ToyPush.
Mathieu Lobet 2016 - 2017   Plasma
La Maison de la Simulation (CEA) Accelerated the Warp NESAP code for Cori including introduction of OpenMP, memory tiling and vectorization for field interpolation, push and charge depositions steps.
Tareq Malas 2016 - 2017   Computer Science (Stencil Applications) Intel Performance optimization of the EMGEO application for the Cori system at NERSC and strategies for MPI communication/computation overlapping.
Bill Arndt 2016 - 2018   Computer Science (Bio-informatics Algorithms) LBNL NERSC Accelerated Cori readiness for HMMER, E3SM, and MPAS codes. Sped up halo neighbor exchanges and threaded memory management in MPAS-Ocean.
Zahra Ronaghi 2017 - 2018   Biomedical Engineering (Masters in Electrical Engineering) NVIDIA Accelerated grid reconstruction methods in the TomoPy tomographic reconstruction code, as well as developed/optimized the Ice Cube machine learning-based event classifier
Jonathan Madsen 2017 - 2019   Nuclear Engineering AMD Accelerated iterative reconstruction methods in the TomoPy iterative tomographic reconstruction codes.
Kevin Gott 2017 - 2019   Mechanical Engineering LBNL NERSC Accelerated Cori and Perlmutter readiness for AMReX and PARSEC.
Rahul Gayatri 2017 - 2018   Computer Science (HPC Parallelism) LBNL NERSC Produced performance portability case-studies performance and models with OpenMP, OpenACC, Kokkos and Raja. Analyzed current state of the art of performance portability.
Laurie Stephey 2017 - 2021   Plasma Physics / Fusion LBNL NERSC Accelerating Python spectroscopic extraction code in the Dark Energy Spectroscopic Instrument (DESI) pipeline
Yunsong Wang 2018 - 2020   Computer Science (Nuclear Physics Applications) NVIDIA ATLAS Cori and Perlmutter readiness including performance analysis of Geant code; multi-node MPI scaling of the ATLAS data analysis pipeline
Yan Zhang 2019 - 2021   Electrical Engineering (Signal Processing) Velodyne Lidar Accelerating progress on NESAP for learning application LSSTNET
Muaaz Awan 2019 - 2020 Western Michigan University Computer Science LBNL NERSC Accelerating progress on Perlmutter GPU readiness for NESAP application Exabiome
Brandon Wood 2019 - 2021 UC Berkeley Applied Physics Facebook Developing, tuning, and scaling graph neural networks to accelerate catalyst discovery; part of the Open Catalyst Project.
Neil Mehta 2020 - 2021 UIUC Aerospace Engineering LBNL NERSC Optimizing particle-based codes (molecular dynamics), C++/Python hybrid codes, Machine learning profiling analysis, Roofline analysis.
Michael Rowan 2019 - 2021 Harvard Physics AMD Improving performance of the Exascale Computing Project advanced particle-in-cell code WarpX, with focus on load balancing. Performance benchmarking and modeling for AI/ML workloads run on HPC platforms.
Hugo Brunie 2019 - 2021 Bordeaux University Computer Science CEA (France) Develop methods and tool to help NERSC users optimize their code with mixed precision tuning optimizations. Current focus on mixed precision tuning for adaptive grid refinement in ASGARD (PI David Green, ORNL). Focus has been made before on NESAP for simulation applications: PeleC (LBL), CCTBX (LBL).
Dossay Oryspayev 2019 - 2020 Iowa State Computer Engineering Brookhaven National Lab GPU optimizations of methods used in the Many Fermion DyNamics (MFDN) application.
Oisin Creaner 2019 - 2021 Institute of Technology, Tallaght, Dublin Computational Astrophysics Dublin Institute for Advanced Studies Enabling GPU simulations of LZ Dark Matter detector. This involves industrial collaboration to upgrade existing Opticks software and fitting this software into the LZ framework.
Ozgur Cekmer 2020 - 2021 University of Tennessee-Knoxville >Mechanical Engineering CSIRO Accelerator performance analysis and optimization of the Adaptive Sparse Grid Discretization (ASGARD) project programming framework.
Amanda Dufek 2020 - 2021 National Laboratory for Scientific Computing (LNCC), Brazil Computational Modeling LBNL NERSC Enhanced roofline modeling including data movement across the HPC interconnect and the GPU bus.
Raphael Prat 2020 - 2021 Bordeaux University Computer Science CEA (France) Stencil-based applications in HPC context. Current project: Optimization of the Proto middleware (used by Chombo4, for example) on GPU supercomputers such as Summit, Tulip or Perlmutter while providing portability and a user-friendly design.
Jaideep Pathak 2020 - 2021 UMD   NVIDIA Developing novel techniques for augmenting computational fluid dynamics simulations with state-of-the-art machine learning models.
Daniel Margala 2020 - 2021 UC Irvine Physics LBNL NERSC Porting the DESI spectral extraction pipeline, a Python-based NESAP for Data application, to use GPUs via CuPy and Numba CUDA.
Muhammad Haseeb 2023 - 2024 Florida International University Computer Science NVIDIA C++ based programming model development and evolution. GPU-acceleration of WarpX/AMReX on Perlmutter.