About

Dr. Jenny Lin is an assistant professor at the Kahlert School of Computing at the University of Utah. Her research focuses on graphics with an interest in physical fabrication and the computational tools built for it. In her spare time, she enjoys scribbling with fountain pens and playing questionable mobile games.

Research topics

• Computer Science
• Parallel computing
• Programming language
• Engineering
• Operating system
• Computer graphics (images)
• Theoretical computer science
• Algorithm
• Mathematics
• Embedded system
• Computational science
• Geometry

Selected publications

• A Survey of Distributed Asynchronous Many-Task Models and Their Applications

  2025-12-23

  articleOpen access

  Asynchronous many-task (AMT) runtime systems have become an important paradigm for expressing fine-grained parallelism and managing asynchrony in high-performance computing (HPC). Originating from early dataflow concepts, AMTs have evolved to enable dynamic task generation, explicit dependency management, and asynchronous execution, facilitating the overlap of computation and communication. These capabilities address the limitations of traditional bulk-synchronous models, such as those employed in MPI+X, which can struggle with irregular, adaptive, or data-driven workloads. This survey provides a comprehensive overview of representative distributed AMT systems—including Charm++, HPX, Legion, PaRSEC, Uintah, Chapel, and StarPU—focusing on their design principles, execution models, and runtime mechanisms for scheduling, communication, and synchronization. We examine how these systems tackle key challenges such as load imbalance, runtime overheads, programmability, and performance portability. In addition, the paper discusses application domains where AMTs have demonstrated tangible benefits and highlights the conditions under which their use is most advantageous. The goal of this survey is to equip researchers and practitioners with a clear understanding of distributed AMT models and to provide guidance for selecting and applying the most suitable runtime system for specific computational objectives.

  PublisherOA PDFDOI

• Lessons Learned and Scalability Achieved When Porting Uintah to DOE Exascale Systems

  Lecture notes in computer science · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Issue Information

  International Journal for Numerical Methods in Fluids · 2024-03-04

  paratextOpen access
  PublisherOA PDFDOI

• An Illustration of Extending Hedgehog to Multi-Node GPU Architectures Using GEMM

  SN Computer Science · 2024-06-15 · 1 citations

  articleSenior author
  PublisherDOI

• Making Uintah Performance Portable for Department of Energy Exascale Testbeds

  Lecture notes in computer science · 2024-01-01 · 1 citations

  book-chapterOpen accessSenior author
  PublisherOA PDFDOI

• Algorithm xxxx: HiPPIS A High-Order Positivity-Preserving Mapping Software for Structured Meshes

  arXiv (Cornell University) · 2023-10-13

  preprintOpen accessSenior author

  Polynomial interpolation is an important component of many computational problems. In several of these computational problems, failure to preserve positivity when using polynomials to approximate or map data values between meshes can lead to negative unphysical quantities. Currently, most polynomial-based methods for enforcing positivity are based on splines and polynomial rescaling. The spline-based approaches build interpolants that are positive over the intervals in which they are defined and may require solving a minimization problem and/or system of equations. The linear polynomial rescaling methods allow for high-degree polynomials but enforce positivity only at limited locations (e.g., quadrature nodes). This work introduces open-source software (HiPPIS) for high-order data-bounded interpolation (DBI) and positivity-preserving interpolation (PPI) that addresses the limitations of both the spline and polynomial rescaling methods. HiPPIS is suitable for approximating and mapping physical quantities such as mass, density, and concentration between meshes while preserving positivity. This work provides Fortran and Matlab implementations of the DBI and PPI methods, presents an analysis of the mapping error in the context of PDEs, and uses several 1D and 2D numerical examples to demonstrate the benefits and limitations of HiPPIS.

  PublisherOA PDFDOI

• Issue Information

  International Journal for Numerical Methods in Fluids · 2023-09-06

  paratextOpen access
  PublisherOA PDFDOI

• Extending Hedgehog’s Dataflow Graphs to Multi-node GPU Architectures

  Lecture notes in computer science · 2023-01-01 · 1 citations

  book-chapter
  PublisherDOI

• Issue Information

  International Journal for Numerical Methods in Fluids · 2023-02-07 · 1 citations

  paratextOpen access
  PublisherOA PDFDOI

• Computational Error Estimation for the Material Point Method in 1D and 2D

  2023-01-01

  articleOpen access1st authorCorresponding

  The Material Point Method (MPM) is widely used for challenging applica tions in engineering, and animation. The complexity of the method makes error estimation challenging. Error analysis of a simple MPM method is undertaken and the global error is shown to be first order in space and time for a widely-used variant of the method. Computational experiments illustrate the estimated accuracy.

  PublisherOA PDFDOI

Recent grants

• XPS: CLCCA (XPS: DSD) Future Extreme Scale Frameworks using DSL and ERTS

  NSF · $700k · 2013–2017

• SDCI HPC: Improvement and Release of the Uintah Computational Framework

  NSF · $704k · 2007–2011

• PetaApps

  NSF · $999k · 2009–2015

Frequent coauthors

• Alan Humphrey

  University of Utah

  36 shared

• Qingyu Meng

  Civil Aviation Management Institute of China

  29 shared

• Todd Harman

  University of Utah

  23 shared

• Christopher E. Goodyer

  ARM (United Kingdom)

  21 shared

• Peter K. Jimack

  20 shared

• L. E. Scales

  Shell (Netherlands)

  19 shared

• Robert M. Kirby

  18 shared

• Peter M. Dew

  15 shared

Labs

• Grad-CS WomenPI

  Not provided