libCEED provides fast algebra for element-based discretizations, designed for performance portability, run-time flexibility, and clean embedding in higher level libraries and applications. It offers a C99 interface as well as bindings for Fortran, Python, Julia, and Rust. While our focus is on high-order finite elements, the approach is mostly algebraic and thus applicable to other discretizations in factored form, as explained in the user manual and API implementation portion of the documentation.
One of the challenges with high-order methods is that a global sparse matrix is no longer a good representation of a high-order linear operator, both with respect to the FLOPs needed for its evaluation, as well as the memory transfer needed for a matvec. Thus, high-order methods require a new "format" that still represents a linear (or more generally non-linear) operator, but not through a sparse matrix.
The goal of libCEED is to propose such a format, as well as supporting implementations and data structures, that enable efficient operator evaluation on a variety of computational device types (CPUs, GPUs, etc.). This new operator description is based on algebraically factored form, which is easy to incorporate in a wide variety of applications, without significant refactoring of their own discretization infrastructure.
The repository is part of the CEED software suite, a collection of software benchmarks, miniapps, libraries and APIs for efficient exascale discretizations based on high-order finite element and spectral element methods. See http://github.com/ceed for more information and source code availability.
The CEED research is supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two U.S. Department of Energy organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, in support of the nation’s exascale computing imperative.
For more details on the CEED API see the user manual.
% gettingstarted-inclusion-marker
The CEED library, libceed, is a C99 library with no required dependencies, and
with Fortran, Python, Julia, and Rust interfaces. It can be built using:
make
or, with optimization flags:
make OPT='-O3 -march=skylake-avx512 -ffp-contract=fast'
These optimization flags are used by all languages (C, C++, Fortran) and this makefile variable can also be set for testing and examples (below).
The library attempts to automatically detect support for the AVX instruction set using gcc-style compiler options for the host. Support may need to be manually specified via:
make AVX=1
or:
make AVX=0
if your compiler does not support gcc-style options, if you are cross compiling, etc.
To enable CUDA support, add CUDA_DIR=/opt/cuda or an appropriate directory
to your make invocation. To enable HIP support, add HIP_DIR=/opt/rocm or
an appropriate directory. To store these or other arguments as defaults for
future invocations of make, use:
make configure CUDA_DIR=/usr/local/cuda HIP_DIR=/opt/rocm OPT='-O3 -march=znver2'
which stores these variables in config.mk.
The Fortran interface is built alongside the library automatically.
Python users can install using:
pip install libceed
or in a clone of the repository via pip install ..
Julia users can install using:
$ julia
julia> ]
pkg> add LibCEED
See the LibCEED.jl documentation for more information.
Rust users can include libCEED via Cargo.toml:
[dependencies]
libceed = { git = "https://github.com/CEED/libCEED", branch = "main" }See the Cargo documentation for details.
The test suite produces TAP output and is run by:
make test
or, using the prove tool distributed with Perl (recommended):
make prove
There are multiple supported backends, which can be selected at runtime in the examples:
| CEED resource | Backend | Deterministic Capable |
|---|---|---|
| CPU Native | ||
/cpu/self/ref/serial |
Serial reference implementation | Yes |
/cpu/self/ref/blocked |
Blocked reference implementation | Yes |
/cpu/self/opt/serial |
Serial optimized C implementation | Yes |
/cpu/self/opt/blocked |
Blocked optimized C implementation | Yes |
/cpu/self/avx/serial |
Serial AVX implementation | Yes |
/cpu/self/avx/blocked |
Blocked AVX implementation | Yes |
| CPU Valgrind | ||
/cpu/self/memcheck/* |
Memcheck backends, undefined value checks | Yes |
| CPU LIBXSMM | ||
/cpu/self/xsmm/serial |
Serial LIBXSMM implementation | Yes |
/cpu/self/xsmm/blocked |
Blocked LIBXSMM implementation | Yes |
| CUDA Native | ||
/gpu/cuda/ref |
Reference pure CUDA kernels | Yes |
/gpu/cuda/shared |
Optimized pure CUDA kernels using shared memory | Yes |
/gpu/cuda/gen |
Optimized pure CUDA kernels using code generation | No |
| HIP Native | ||
/gpu/hip/ref |
Reference pure HIP kernels | Yes |
/gpu/hip/shared |
Optimized pure HIP kernels using shared memory | Yes |
/gpu/hip/gen |
Optimized pure HIP kernels using code generation | No |
| MAGMA | ||
/gpu/cuda/magma |
CUDA MAGMA kernels | No |
/gpu/cuda/magma/det |
CUDA MAGMA kernels | Yes |
/gpu/hip/magma |
HIP MAGMA kernels | No |
/gpu/hip/magma/det |
HIP MAGMA kernels | Yes |
| OCCA | ||
/*/occa |
Selects backend based on available OCCA modes | Yes |
/cpu/self/occa |
OCCA backend with serial CPU kernels | Yes |
/cpu/openmp/occa |
OCCA backend with OpenMP kernels | Yes |
/gpu/cuda/occa |
OCCA backend with CUDA kernels | Yes |
/gpu/hip/occa~ |
OCCA backend with HIP kernels | Yes |
The /cpu/self/*/serial backends process one element at a time and are intended for meshes
with a smaller number of high order elements. The /cpu/self/*/blocked backends process
blocked batches of eight interlaced elements and are intended for meshes with higher numbers
of elements.
The /cpu/self/ref/* backends are written in pure C and provide basic functionality.
The /cpu/self/opt/* backends are written in pure C and use partial e-vectors to improve performance.
The /cpu/self/avx/* backends rely upon AVX instructions to provide vectorized CPU performance.
The /cpu/self/memcheck/* backends rely upon the Valgrind Memcheck tool
to help verify that user QFunctions have no undefined values. To use, run your code with
Valgrind and the Memcheck backends, e.g. valgrind ./build/ex1 -ceed /cpu/self/ref/memcheck. A
'development' or 'debugging' version of Valgrind with headers is required to use this backend.
This backend can be run in serial or blocked mode and defaults to running in the serial mode
if /cpu/self/memcheck is selected at runtime.
The /cpu/self/xsmm/* backends rely upon the LIBXSMM package
to provide vectorized CPU performance. If linking MKL and LIBXSMM is desired but
the Makefile is not detecting MKLROOT, linking libCEED against MKL can be
forced by setting the environment variable MKL=1.
The /gpu/cuda/* backends provide GPU performance strictly using CUDA.
The /gpu/hip/* backends provide GPU performance strictly using HIP. They are based on
the /gpu/cuda/* backends. ROCm version 3.6 or newer is required.
The /gpu/*/magma/* backends rely upon the MAGMA package.
To enable the MAGMA backends, the environment variable MAGMA_DIR must point to the top-level
MAGMA directory, with the MAGMA library located in $(MAGMA_DIR)/lib/.
By default, MAGMA_DIR is set to ../magma; to build the MAGMA backends
with a MAGMA installation located elsewhere, create a link to magma/ in libCEED's parent
directory, or set MAGMA_DIR to the proper location. MAGMA version 2.5.0 or newer is required.
Currently, each MAGMA library installation is only built for either CUDA or HIP. The corresponding
set of libCEED backends (/gpu/cuda/magma/* or /gpu/hip/magma/*) will automatically be built
for the version of the MAGMA library found in MAGMA_DIR.
Users can specify a device for all CUDA, HIP, and MAGMA backends through adding :device_id=#
after the resource name. For example:
/gpu/cuda/gen:device_id=1
The /*/occa backends rely upon the OCCA package to provide
cross platform performance. To enable the OCCA backend, the environment variable OCCA_DIR must point
to the top-level OCCA directory, with the OCCA library located in the ${OCCA_DIR}/lib (By default,
OCCA_DIR is set to ../occa).
Additionally, users can pass specific OCCA device properties after setting the CEED resource. For example:
"/*/occa:mode='CUDA',device_id=0"
Bit-for-bit reproducibility is important in some applications.
However, some libCEED backends use non-deterministic operations, such as atomicAdd for increased performance.
The backends which are capable of generating reproducible results, with the proper compilation options, are highlighted in the list above.
libCEED comes with several examples of its usage, ranging from standalone C
codes in the /examples/ceed directory to examples based on external packages,
such as MFEM, PETSc, and Nek5000. Nek5000 v18.0 or greater is required.
To build the examples, set the MFEM_DIR, PETSC_DIR, and
NEK5K_DIR variables and run:
cd examples/
% running-examples-inclusion-marker
# libCEED examples on CPU and GPU
cd ceed/
make
./ex1-volume -ceed /cpu/self
./ex1-volume -ceed /gpu/cuda
./ex2-surface -ceed /cpu/self
./ex2-surface -ceed /gpu/cuda
cd ..
# MFEM+libCEED examples on CPU and GPU
cd mfem/
make
./bp1 -ceed /cpu/self -no-vis
./bp3 -ceed /gpu/cuda -no-vis
cd ..
# Nek5000+libCEED examples on CPU and GPU
cd nek/
make
./nek-examples.sh -e bp1 -ceed /cpu/self -b 3
./nek-examples.sh -e bp3 -ceed /gpu/cuda -b 3
cd ..
# PETSc+libCEED examples on CPU and GPU
cd petsc/
make
./bps -problem bp1 -ceed /cpu/self
./bps -problem bp2 -ceed /gpu/cuda
./bps -problem bp3 -ceed /cpu/self
./bps -problem bp4 -ceed /gpu/cuda
./bps -problem bp5 -ceed /cpu/self
./bps -problem bp6 -ceed /gpu/cuda
cd ..
cd petsc/
make
./bpsraw -problem bp1 -ceed /cpu/self
./bpsraw -problem bp2 -ceed /gpu/cuda
./bpsraw -problem bp3 -ceed /cpu/self
./bpsraw -problem bp4 -ceed /gpu/cuda
./bpsraw -problem bp5 -ceed /cpu/self
./bpsraw -problem bp6 -ceed /gpu/cuda
cd ..
cd petsc/
make
./bpssphere -problem bp1 -ceed /cpu/self
./bpssphere -problem bp2 -ceed /gpu/cuda
./bpssphere -problem bp3 -ceed /cpu/self
./bpssphere -problem bp4 -ceed /gpu/cuda
./bpssphere -problem bp5 -ceed /cpu/self
./bpssphere -problem bp6 -ceed /gpu/cuda
cd ..
cd petsc/
make
./area -problem cube -ceed /cpu/self -degree 3
./area -problem cube -ceed /gpu/cuda -degree 3
./area -problem sphere -ceed /cpu/self -degree 3 -dm_refine 2
./area -problem sphere -ceed /gpu/cuda -degree 3 -dm_refine 2
cd fluids/
make
./navierstokes -ceed /cpu/self -degree 1
./navierstokes -ceed /gpu/cuda -degree 1
cd ..
cd solids/
make
./elasticity -ceed /cpu/self -mesh [.exo file] -degree 2 -E 1 -nu 0.3 -problem Linear -forcing mms
./elasticity -ceed /gpu/cuda -mesh [.exo file] -degree 2 -E 1 -nu 0.3 -problem Linear -forcing mms
cd ..For the last example shown, sample meshes to be used in place of
[.exo file] can be found at https://github.com/jeremylt/ceedSampleMeshes
The above code assumes a GPU-capable machine with the OCCA backend
enabled. Depending on the available backends, other CEED resource
specifiers can be provided with the -ceed option. Other command line
arguments can be found in examples/petsc.
% benchmarks-marker
A sequence of benchmarks for all enabled backends can be run using:
make benchmarks
The results from the benchmarks are stored inside the benchmarks/ directory
and they can be viewed using the commands (requires python with matplotlib):
cd benchmarks
python postprocess-plot.py petsc-bps-bp1-*-output.txt
python postprocess-plot.py petsc-bps-bp3-*-output.txt
Using the benchmarks target runs a comprehensive set of benchmarks which may
take some time to run. Subsets of the benchmarks can be run using the scripts in the benchmarks folder.
For more details about the benchmarks, see the benchmarks/README.md file.
To install libCEED, run:
make install prefix=/usr/local
or (e.g., if creating packages):
make install prefix=/usr DESTDIR=/packaging/path
The usual variables like CC and CFLAGS are used, and optimization flags
for all languages can be set using the likes of OPT='-O3 -march=native'. Use
STATIC=1 to build static libraries (libceed.a).
To install libCEED for Python, run:
pip install libceed
with the desired setuptools options, such as --user.
In addition to library and header, libCEED provides a pkg-config
file that can be used to easily compile and link.
For example, if
$prefix is a standard location or you set the environment variable
PKG_CONFIG_PATH:
cc `pkg-config --cflags --libs ceed` -o myapp myapp.c
will build myapp with libCEED. This can be used with the source or
installed directories. Most build systems have support for pkg-config.
You can reach the libCEED team by emailing [email protected] or by leaving a comment in the issue tracker.
If you utilize libCEED please cite:
@article{libceed-joss-paper,
author = {Jed Brown and Ahmad Abdelfattah and Valeria Barra and Natalie Beams and Jean Sylvain Camier and Veselin Dobrev and Yohann Dudouit and Leila Ghaffari and Tzanio Kolev and David Medina and Will Pazner and Thilina Ratnayaka and Jeremy Thompson and Stan Tomov},
title = {{libCEED}: Fast algebra for high-order element-based discretizations},
journal = {Journal of Open Source Software},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {63},
pages = {2945},
doi = {10.21105/joss.02945}
}
@misc{libceed-user-manual,
author = {Abdelfattah, Ahmad and
Barra, Valeria and
Beams, Natalie and
Brown, Jed and
Camier, Jean-Sylvain and
Dobrev, Veselin and
Dudouit, Yohann and
Ghaffari, Leila and
Kolev, Tzanio and
Medina, David and
Pazner, Will and
Ratnayaka, Thilina and
Thompson, Jeremy L and
Tomov, Stanimire},
title = {{libCEED} User Manual},
month = jul,
year = 2021,
publisher = {Zenodo},
version = {0.9.0},
doi = {10.5281/zenodo.5077489}
}
For libCEED's Python interface please cite:
@InProceedings{libceed-paper-proc-scipy-2020,
author = {{V}aleria {B}arra and {J}ed {B}rown and {J}eremy {T}hompson and {Y}ohann {D}udouit},
title = {{H}igh-performance operator evaluations with ease of use: lib{C}{E}{E}{D}'s {P}ython interface},
booktitle = {{P}roceedings of the 19th {P}ython in {S}cience {C}onference},
pages = {85 - 90},
year = {2020},
editor = {{M}eghann {A}garwal and {C}hris {C}alloway and {D}illon {N}iederhut and {D}avid {S}hupe},
doi = {10.25080/Majora-342d178e-00c}
}
The BiBTeX entries for these references can be found in the
doc/bib/references.bib file.
The following copyright applies to each file in the CEED software suite, unless otherwise stated in the file:
Copyright (c) 2017, Lawrence Livermore National Security, LLC. Produced at the Lawrence Livermore National Laboratory. LLNL-CODE-734707. All Rights reserved.
See files LICENSE and NOTICE for details.