JLSE Projects by Category:

Application Performance & Science

Data-driven Weather Prediction (PI: Gianmarco Mengalldo, National University of Singapore)

Scalable Reinforcement Learning on Electric Grid Operations

Monte Carlo Particle Transport on GPU-based Systems

Braid (PI: Jie Liu, Argonne National Laboratory)

Performance portability – Crossroads (PI: Hai Ah Nam, Los Alamos National Laboratory)

Memory Optimization and Topology Management (PI: Nicolas Denoyelle, Argonne National Laboratory)

LOSF: Lots of Small Files movement (PI: Zhengchun Liu, Argonne National Laboratory)

Performance and Power Characterization of HPC Applications on Heterogeneous Memory System (PI: Zhiling Lan, Illinois Institute of Technology)

PBS Testing (PI: William Allcock, LCF)

Developing and Optimizing the error-bounded lossy compression on GPU (PI: Sheng Di, MCS)

ChemNet (PI: Pinaki Pal, ES)

A Lightweight Low-level Threading Framework (PI: Shintaro Iwasaki, MCS)

ALCF Aurora Early Science Program (PI: Tim Williams, CPS)

All Projects using the Comanche System (PI: John Linford, ARM)

Exploring and Modeling Tradeoffs Among Time, Power and Resilience on Heterogeneous Systems (PI: Xingfu Wu, MCS)

Computationally Assisted Experimental Chemical Reaction Dynamics and Kinetics in the Gas Phase (PI: Kirill Prozument, CSE)

Performance Optimization of PETSc on KNL (PI: Hong Zhang, MCS)

Compressible Multiphase Turbulence (PI: Scott Parker, LCF)

Coupled Monte Carlo Neutronics and Fluid Flow Simulation of Small Modular Reactors (PI: Andrew Siegel, MCS)

Toward Metascalable Quantum and Reactive Molecular Dynamics Simulations (PI: Nichols A. Romero, LCF)

3D Method of Characteristics Reactor Simulation: Multi-core Studies and Application Development (PI: John Tramm, MCS)

Continuing Improvement and Testing of LLVM’s PowerPC Backend (PI: Hal Finkel, LCF)

Analysis of Spectral Element Solver Performance Across Multiple Architectures (PI: Scott Parker, LCF)

Analysis of Continuous and Discontinuous Finite Element Based Particle Transport Methods on Heterogeneous Resources (PI: Micheal Smith, NE)

A Design Framework of Inverse Modeling using Theory-guided Machine Learning

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