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 Mechanics	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 Physics	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.