HPC Forge

HPC Forge is a high-performance computing research lab at the University of California, Irvine. We aim to advance computational science and engineering using parallel/distributed computing and scientific machine learning. Our target platforms span single-node to large-scale systems (i.e., supercomputers). Check out our recent Publications to see what we do.

We are always looking for interested and motivated students/postdocs to join our team. If you are interested in joining our research lab, please email your CV and one representative publication (if any) to amowli@uci.edu.

Hi there!

I’m an associate professor in the Department of Electrical Engineering and Computer Science at UC Irvine. My research is in the area of high-performance computing and I lead the HPC Forge research lab. I received my Ph.D in Computational Science and Engineering from Georgia Tech in 2013 in the HPC Garage. Prior to joining UCI, I was a research scientist at MIT CSAIL, where I worked on the X-Stack (exascale software stack) project.

Interests

  • High-performance computing
  • Scientific machine learning
  • Neural PDE solvers
  • Performance analysis and modeling

Education

  • PhD in Computational Science and Engineering, 2013

    Georgia Institute of Technology

  • BE in Computer Science and Engineering, 2007

    Anna University

The Lab

PhD Students

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Arthur Feeney

PhD student, EECS, 2022-present

  • MS in Computer Science, University of Massachusetts Amherst, 2021

  • BS in Computer Science, Trinity University, 2019

Current research: Domain Decomposition Methods for Neural PDE Solvers

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Zitong Li

PhD student, EECS, 2022-present

  • MS in Computer Science, Wake Forest University, 2022

  • BS in Computer Science, University of Nebraska-Lincoln, 2018

Current research: Domain Decomposition Methods for Neural Poisson Solvers

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Shakeel Hassan

PhD student, EECS, 2022-present

  • BTech in Electronics and Communication Engineering, National Institute of Technology Trichy, 2020

Current research: Hybrid Solvers Fusing Neural Operators with Numerical Methods

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Saba Farahani

PhD student, EECS, 2023-present (co-advised w/ Yoonjin Won)

  • MS in Electrical Engineering, Sharif University of Technology, 2022

  • BS in Electrical Engineering, University of Tehran, 2019

Current research: Scientific Machine Learning using Multimodal Data

MS Students

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Sebastian Barschkis

MS student, EECS, 2021-present

  • BS in Computer Science, Ludwig Maximilian University of Munich, 2018

Current research: Learning Hidden Fluid Dynamics using PINN

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Srikar Arani

MS student, EECS, 2022-present

  • BS in Computer Engineering, University of California Santa Barbara, 2022

Current research: Scientific Machine Learning using Event Data

Alumni

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Octavi Obiols Sales

MAE PhD (2022)

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Behnam Pourghassemi

EECS PhD (2021)

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Hengjie Wang

MAE PhD (2021)

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Shu-Mei Tseng

EECS MS (2021)

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Rohit Zambre

EECS PhD (2020)

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Laleh Beni

CS PhD (2019)

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Ferran Marti

Postdoctoral Scholar (2017 - 2018)

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Bahareh Davani

EECS MS (2016)

Recent Publications

BubbleML: A Multiphysics Dataset and Benchmarks for Machine Learning

Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers

Artificial Intelligence for Liquid-Vapor Phase-Change Heat Transfer

ADARNet: Deep Learning Predicts Adaptive Mesh Refinement

Lessons Learned on MPI+Threads Communication

Projects

HiPer

A CFD solver for high-performance turbulent flow simulations

Machine & Deep Learning

HPC for accelerating ML/DL and DL for science

Recent & Upcoming Talks

Transferable Deep Learning Surrogates for Solving PDEs

Only Relative Speed Matters -- Virtual Causal Profiling

Scalable Web Performance Analysis Using Causal Profiling

On the Limits of Parallelizing Convolutional Neural Networks on GPUs

CFDNet - A deep learning-based accelerator for fluid simulations