About

Janice Brown is a Professor of Japanese and the Director of Graduate Studies in Japanese at the University of Colorado Boulder. She received her BA in Japanese language, as well as her MA and PhD in Asian Studies with a focus on Modern Japanese Literature, from the University of British Columbia. She has been part of the University of Colorado Boulder faculty since 2007. Prior to her current position, she served as Professor and Chair of East Asian Studies at the University of Alberta. Her scholarly work includes authorship of books such as 'Hayashi Fumiko: I Saw A Pale Horse and Selected Poems from Diary of a Vagabond' and 'Tarnished Words: The Poetry of Ōba Minako.' Her areas of specialization encompass modern Japanese women writers, modern and contemporary Japanese women’s poetry, Japanese literary modernism, and modern Japanese fiction. Her research interests extend to modern and contemporary Japanese women’s poetry and fiction, gender, sexuality, and the body, contemporary popular and visual culture in Japan, literary modernism, and critical posthumanities.

Research topics

• Computer Science
• Parallel computing
• Computational science
• Computer architecture
• Distributed computing
• Programming language
• Operating system
• Mathematical optimization
• Mathematics
• Theoretical computer science

Selected publications

• Efficient exascale discretizations: High-order finite element methods

  The International Journal of High Performance Computing Applications · 2021 · 55 citations

  • Computer Science
  • Computer Science
  • Computational science

  Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

  DOI

• Toward performance-portable PETSc for GPU-based exascale systems

  Parallel Computing · 2021 · 55 citations

  • Computer Science
  • Computer Science
  • Parallel computing
  PublisherOA PDFDOI

• Toward Performance-Portable PETSc for GPU-based Exascale Systems

  arXiv (Cornell University) · 2020 · 6 citations

  • Computer Science
  • Computer Science
  • Parallel computing

  The Portable Extensible Toolkit for Scientific computation (PETSc) library delivers scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization.The PETSc design for performance portability addresses fundamental GPU accelerator challenges and stresses flexibility and extensibility by separating the programming model used by the application from that used by the library, and it enables application developers to use their preferred programming model, such as Kokkos, RAJA, SYCL, HIP, CUDA, or OpenCL, on upcoming exascale systems. A blueprint for using GPUs from PETSc-based codes is provided, and case studies emphasize the flexibility and high performance achieved on current GPU-based systems.

  PublisherOA PDFDOI

• Scalability of high-performance PDE solvers

  The International Journal of High Performance Computing Applications · 2020 · 73 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Computational science

  Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.

  DOI

Frequent coauthors

• Matthew G. Knepley

  University at Buffalo, State University of New York

  56 shared

• Jeremy Thompson

  University of Colorado Boulder

  32 shared

• Mark F. Adams

  Saint Mary's University

  25 shared

• Barry Smith

  Argonne National Laboratory

  23 shared

• Valeria Barra

  Policlinico S.Orsola-Malpighi

  23 shared

• Ravi Samtaney

  King Abdullah University of Science and Technology

  16 shared

• Paul Fischer

  16 shared

• Laëtitia Le Pourhiet

  15 shared

Education

• Ph.D., Asian Studies (Modern Japanese Literature)

  University of British Columbia

• M.A., Asian Studies (Modern Japanese Literature)

  University of British Columbia

• B.A., Japanese language

  University of British Columbia

Similar researchers at University of Colorado Boulder

• Jose-Luis Jimenezh-233
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• Roy Parkerh-161
• Kristi S. Ansethh-159
• Thomas R. Cechh-156