Metadata-Version: 2.1
Name: libceed
Version: 0.12.0
Summary: libCEED: Code for Efficient Extensible Discretization
Home-page: https://libceed.org
Download-URL: https://github.com/CEED/libCEED/releases
Author: libCEED Team
Author-email: ceed-users@llnl.gov
License: BSD 2
Project-URL: Bug Tracker, https://github.com/CEED/libCEED/issues
Project-URL: Documentation, https://libceed.org
Project-URL: Source Code, https://github.com/CEED/libCEED
Keywords: libCEED
Platform: POSIX
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: C
Classifier: Programming Language :: C++
Classifier: Programming Language :: Fortran
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Description-Content-Type: text/x-rst
Provides-Extra: cuda
License-File: LICENSE


libCEED: Code for Efficient Extensible Discretization
=====================================================

libCEED is a lightweight library for expressing and manipulating operators that
arise in high-order element-based discretization of partial differential
equations.  libCEED's representations are much for efficient than assembled
sparse matrices, and can achieve very high performance on modern CPU and GPU
hardware.  This approach is applicable to a broad range of linear and nonlinear
problems, and includes facilities for preconditioning.  libCEED is meant to be
easy to incorporate into existing libraries and applications, and to build new
tools on top of.

libCEED has been developed as part of the DOE Exascale Computing Project
co-design Center for Efficient Exascale Discretizations (CEED).
