Neil Lindquist

NeilLindquist5@gmail.com

Research Interests

Numerical Linear Algebra

Effects of data representation on performance and accuracy

High performance computing

Education

Ph.D. student in Computer Science, University of Tennessee,

  • Advised by Dr. Jack Dongarra

B.A. magna cum laude in Math and Computer Science, Saint John’s University, 2019

Research Experience

Intern - MathWorks (May 2022 through August 2022)

  • Contributed to the development of MATLAB linear algebra routines.

Givens Associate - Argonne National Laboratory (May 2021 through August 2021)

  • Ported interpolation routines to GPU accelerators using the OCCA runtime in NekRS, a spectral-element based fluid dynamics code. The added routines where then used to implement both particle tracking and overlapping mesh functionalities.

Graduate Research Assistant - University of Tennessee (July 2019 through the present)

  • Experimenting with using Random Butterfly Transforms to replace pivoting in LU factorization for SLATE, a distributed, GPU-accelerated, dense linear algebra library.
  • Experimented with mixing double and single floating point precision in GMRES, a sparse, iterative linear solver.
  • Contributed to a machine learning based workflow for classification of protein structural properties from XFEL diffraction patterns.

Research Assistant - Saint John’s University (May 2017 through May 2019)

  • Explored the use of data compression to improve the performance of Conjugate Gradient, a sparse, iterative linear solver.
  • Tested the effect on performance of using Julia, a high level programming language, to implement distributed, sparse linear algebra codes.

Honors and Awards

Tennessee’s Top 100 Fellowship, University of Tennessee (August 2019 - Present)

Pi Mu Epsilon Mathematics Honor Society (inducted May 2019)

Phi Beta Kappa Honors Society (inducted April 2019)

Eagle Scout (awarded June 2014)

Publications

N. Lindquist, P. Luszczek, and J. Dongarra, “Accelerating Restarted GMRES with Mixed Precision Arithmetic,” IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS) Special Section on Innovative R&D toward the Exascale Era, DOI: 10.1109/TPDS.2021.3090757

N. Lindquist, P. Luszczek, and J. Dongarra, “Replacing Pivoting in Distributed Gaussian Elimination with Randomized Techniques,” presented at the 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA), Atlanta, GA, USA, Nov. 2020, DOI: 10.1109/ScalA51936.2020.00010

P. Luszczek, Y. Tsai, N. Lindquist, H. Anzt, and J. Dongarra, “Scalable data generation for evaluating mixed-precision solvers,” presented at the 2020 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, Sep. 2020, pp. 1–6, DOI: 10.1109/HPEC43674.2020.9286145.

N. Lindquist, P. Luszczek, and J. Dongarra, “Improving the Performance of the GMRES method using Mixed-Precision Techniques,” presented at the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Oak Ridge, TN, USA, Aug. 2020, DOI: 10.1007/978-3-030-63393-6_4

N. Lindquist, “Replicated Computational Results (RCR) Report for ‘Code Generation for Generally Mapped Finite Elements,’” ACM Trans. Math. Softw., vol. 45, no. 4, pp. 42:1–42:7, Dec. 2019, DOI: 10.1145/3360984

Presentations

Replacing Pivoting in Distributed Gaussian Elimination with Randomized Techniques

  • 2021 SIAM Annual Meeting

Multiprecision Approach in GMRES and its Effects on Performance

  • 2021 SIAM Conference on Applied Linear Algebra

Accelerating GMRES via Mixed Precision

  • 12th JLESC workshop

Improve the Performance of GMRES using Mixed Precision

  • 2020 SIAM Conference of Parallel Processing for Scientific Computing

Reducing Memory Access Latencies using Data Compression in Sparse, Iterative Linear Solvers

  • 2019 CSB/SJU Pi Mu Epsilon Conference

Obtaining Performance from a Julia-Implementation of Trilinos Data Librairies

  • 2019 SIAM Conference on Computational Science and Engineering# Research Experience

Teaching Experience

Graduate Teaching Assistant - Scientific Computing For Engineers (Spring 2021)

Graduate Teaching Assistant - Scientific Computing For Engineers (Spring 2020)