scholarly journals PyTrilinos: Recent Advances in the Python Interface to Trilinos

2012 ◽  
Vol 20 (3) ◽  
pp. 311-325 ◽  
Author(s):  
William F. Spotz
Keyword(s):  
Linear Algebra ◽  
Utility Functions ◽  
Ease Of Use ◽  
Linear Solution ◽  
Dense Linear Algebra ◽  
Multilevel Preconditioners ◽  
Front End ◽  
Nonlinear Solvers ◽  
Sparse Linear Algebra ◽  
Python Package

PyTrilinos is a set of Python interfaces to compiled Trilinos packages. This collection supports serial and parallel dense linear algebra, serial and parallel sparse linear algebra, direct and iterative linear solution techniques, algebraic and multilevel preconditioners, nonlinear solvers and continuation algorithms, eigensolvers and partitioning algorithms. Also included are a variety of related utility functions and classes, including distributed I/O, coloring algorithms and matrix generation. PyTrilinos vector objects are compatible with the popular NumPy Python package. As a Python front end to compiled libraries, PyTrilinos takes advantage of the flexibility and ease of use of Python, and the efficiency of the underlying C++, C and Fortran numerical kernels. This paper covers recent, previously unpublished advances in the PyTrilinos package.

Applying the concurrent collections programming model to asynchronous parallel dense linear algebra

2010 ◽  
Vol 45 (5) ◽  
pp. 345-346 ◽  
Author(s):  
Aparna Chandramowlishwaran ◽  
Kathleen Knobe ◽  
Richard Vuduc
Keyword(s):  
Linear Algebra ◽  
Programming Model ◽  
Dense Linear Algebra ◽  
Asynchronous Parallel

Dense linear algebra kernels on heterogeneous platforms: Redistribution issues

2002 ◽  
Vol 28 (2) ◽  
pp. 155-185 ◽  
Author(s):  
Olivier Beaumont ◽  
Arnaud Legrand ◽  
Fabrice Rastello ◽  
Yves Robert
Keyword(s):  
Linear Algebra ◽  
Heterogeneous Platforms ◽  
Dense Linear Algebra

scholarly journals Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators

2018 ◽  
Vol 106 (11) ◽  
pp. 2040-2055 ◽  
Author(s):  
Jack Dongarra ◽  
Mark Gates ◽  
Jakub Kurzak ◽  
Piotr Luszczek ◽  
Yaohung M. Tsai
Keyword(s):  
Linear Algebra ◽  
Hardware Accelerators ◽  
Dense Linear Algebra

Prospectus for a Dense Linear Algebra Software Library

2007 ◽  
pp. 29-1-29-21
Author(s):  
Yozo Hida ◽  
James Demmel ◽  
Julien Langou ◽  
Jakub Kurzak ◽  
Ming Gu ◽  
...  
Keyword(s):  
Linear Algebra ◽  
Software Library ◽  
Dense Linear Algebra

scholarly journals MP-NeRF: A Massively Parallel Method for Accelerating Protein Structure Reconstruction from Internal Coordinates

2021 ◽  
Author(s):  
Eric Alcaide ◽  
Stella Biderman ◽  
Amalio Telenti ◽  
Michael Cyrus Maher
Keyword(s):  
Natural Extension ◽  
Ease Of Use ◽  
Parallel Applications ◽  
Massively Parallel ◽  
Parallel Composition ◽  
Matrix Operations ◽  
Previous State ◽  
Dynamics Simulations ◽  
Protein Structure Reconstruction ◽  
Python Package

The conversion of proteins between internal and cartesian coordinates is a limiting step in many pipelines, such as molecular dynamics simulations and machine learning models. This conversion is typically carried out by sequential or parallel applications of the Natural extension of Reference Frame (NeRF) algorithm. This work proposes a massively parallel NeRF implementation which, depending on the polymer length, achieves speedups between 400-1000x over the previous state-of-the-art NeRF implementation. It accomplishes this by dividing the conversion into three main phases: a parallel composition of the monomer backbone, the assembly of backbone subunits, and the parallel elongation of sidechains; and by batching computations into a minimal number of efficient matrix operations. Special emphasis is placed on reusability and ease of use within diverse pipelines. We open source the code (available at https://github.com/EleutherAI/mp_nerf) and provide a corresponding python package.

Memory Optimizations for Sparse Linear Algebra on GPU Hardware

2021 ◽  
Author(s):  
Aaron Walden ◽  
Mohammad Zubair ◽  
Christopher P. Stone ◽  
Eric J. Nielsen
Keyword(s):  
Linear Algebra ◽  
Sparse Linear Algebra ◽  
Memory Optimizations
Sign in / Sign up

Export Citation Format

Share Document