1# libCEED: the CEED API Library 2 3[](https://travis-ci.org/CEED/libCEED) 4[](https://codecov.io/gh/CEED/libCEED/) 5[](https://opensource.org/licenses/BSD-2-Clause) 6[](https://codedocs.xyz/CEED/libCEED/) 7 8## Code for Efficient Extensible Discretization 9 10This repository contains an initial low-level API library for the efficient 11high-order discretization methods developed by the ECP co-design [Center for 12Efficient Exascale Discretizations (CEED)](http://ceed.exascaleproject.org). 13While our focus is on high-order finite elements, the approach is mostly 14algebraic and thus applicable to other discretizations in factored form, as 15explained in the API documentation portion of the [Doxygen documentation](https://codedocs.xyz/CEED/libCEED/md_doc_libCEEDapi.html). 16 17One of the challenges with high-order methods is that a global sparse matrix is 18no longer a good representation of a high-order linear operator, both with 19respect to the FLOPs needed for its evaluation, as well as the memory transfer 20needed for a matvec. Thus, high-order methods require a new "format" that still 21represents a linear (or more generally non-linear) operator, but not through a 22sparse matrix. 23 24The goal of libCEED is to propose such a format, as well as supporting 25implementations and data structures, that enable efficient operator evaluation 26on a variety of computational device types (CPUs, GPUs, etc.). This new operator 27description is based on algebraically [factored form](https://codedocs.xyz/CEED/libCEED/md_doc_libCEEDapi.html), 28which is easy to incorporate in a wide variety of applications, without significant 29refactoring of their own discretization infrastructure. 30 31The repository is part of the [CEED software suite][ceed-soft], a collection of 32software benchmarks, miniapps, libraries and APIs for efficient exascale 33discretizations based on high-order finite element and spectral element methods. 34See http://github.com/ceed for more information and source code availability. 35 36The CEED research is supported by the [Exascale Computing Project][ecp] 37(17-SC-20-SC), a collaborative effort of two U.S. Department of Energy 38organizations (Office of Science and the National Nuclear Security 39Administration) responsible for the planning and preparation of a [capable 40exascale ecosystem](https://exascaleproject.org/what-is-exascale), including 41software, applications, hardware, advanced system engineering and early testbed 42platforms, in support of the nation’s exascale computing imperative. 43 44For more details on the CEED API see http://ceed.exascaleproject.org/ceed-code/. 45 46## Building 47 48The CEED library, `libceed`, is a C99 library with no external dependencies. It 49can be built using 50 51 make 52 53or, with optimization flags 54 55 make OPT='-O3 -march=skylake-avx512 -ffp-contract=fast' 56 57These optimization flags are used by all languages (C, C++, Fortran) and this 58makefile variable can also be set for testing and examples (below). 59 60The library attempts to automatically detect support for the AVX 61instruction set using gcc-style compiler options for the host. 62Support may need to be manually specified via 63 64 make AVX=1 65 66or 67 68 make AVX=0 69 70if your compiler does not support gcc-style options, if you are cross 71compiling, etc. 72 73## Testing 74 75The test suite produces [TAP](https://testanything.org) output and is run by: 76 77 make test 78 79or, using the `prove` tool distributed with Perl (recommended) 80 81 make prove 82 83## Backends 84 85There are multiple supported backends, which can be selected at runtime in the examples: 86 87| CEED resource | Backend | 88| :----------------------- | :------------------------------------------------ | 89| `/cpu/self/ref/serial` | Serial reference implementation | 90| `/cpu/self/ref/blocked` | Blocked refrence implementation | 91| `/cpu/self/tmpl` | Backend template, delegates to `/cpu/self/ref/blocked` | 92| `/cpu/self/avx/serial` | Serial AVX implementation | 93| `/cpu/self/avx/blocked` | Blocked AVX implementation | 94| `/cpu/self/xsmm/serial` | Serial LIBXSMM implementation | 95| `/cpu/self/xsmm/blocked` | Blocked LIBXSMM implementation | 96| `/cpu/occa` | Serial OCCA kernels | 97| `/gpu/occa` | CUDA OCCA kernels | 98| `/omp/occa` | OpenMP OCCA kernels | 99| `/ocl/occa` | OpenCL OCCA kernels | 100| `/gpu/cuda/ref` | Reference pure CUDA kernels | 101| `/gpu/cuda/reg` | Pure CUDA kernels using one thread per element | 102| `/gpu/magma` | CUDA MAGMA kernels | 103 104 105The `/cpu/self/*/serial` backends process one element at a time and are intended for meshes 106with a smaller number of high order elements. The `/cpu/self/*/blocked` backends process 107blocked batches of eight interlaced elements and are intended for meshes with higher numbers 108of elements. 109 110The `/cpu/self/ref/*` backends are written in pure C and provide basic functionality. 111 112The `/cpu/self/avx/*` backends rely upon AVX instructions to provide vectorized CPU performance. 113 114The `/cpu/self/xsmm/*` backends relies upon the [LIBXSMM](http://github.com/hfp/libxsmm) package 115to provide vectorized CPU performance. 116 117The `/*/occa` backends rely upon the [OCCA](http://github.com/libocca/occa) package to provide 118cross platform performance. 119 120The `/gpu/cuda/*` backends provide GPU performance strictly using CUDA. 121 122The `/gpu/magma` backend relies upon the [MAGMA](https://bitbucket.org/icl/magma) package. 123 124## Examples 125 126libCEED comes with several examples of its usage, ranging from standalone C 127codes in the `/examples/ceed` directory to examples based on external packages, 128such as MFEM, PETSc, and Nek5000. Nek5000 v18.0 or greater is required. 129 130To build the examples, set the `MFEM_DIR`, `PETSC_DIR` and `NEK5K_DIR` variables 131and run: 132 133```console 134# libCEED examples on CPU and GPU 135cd examples/ceed 136make 137./ex1 -ceed /cpu/self 138./ex1 -ceed /gpu/occa 139cd ../.. 140 141# MFEM+libCEED examples on CPU and GPU 142cd examples/mfem 143make 144./bp1 -ceed /cpu/self -no-vis 145./bp1 -ceed /gpu/occa -no-vis 146cd ../.. 147 148# PETSc+libCEED examples on CPU and GPU 149cd examples/petsc 150make 151./bp1 -ceed /cpu/self 152./bp1 -ceed /gpu/occa 153cd ../.. 154 155# Nek+libCEED examples on CPU and GPU 156cd examples/nek5000 157./make-nek-examples.sh 158./run-nek-example.sh -ceed /cpu/self -b 3 159./run-nek-example.sh -ceed /gpu/occa -b 3 160cd ../.. 161``` 162 163The above code assumes a GPU-capable machine with the OCCA backend 164enabled. Depending on the available backends, other Ceed resource specifiers can 165be provided with the `-ceed` option. 166 167## Benchmarks 168 169A sequence of benchmarks for all enabled backends can be run using 170 171```console 172make benchmarks 173``` 174 175The results from the benchmarks are stored inside the `benchmarks/` directory 176and they can be viewed using the commands (requires python with matplotlib): 177 178```console 179cd benchmarks 180python postprocess-plot.py petsc-bp1-*-output.txt 181python postprocess-plot.py petsc-bp3-*-output.txt 182``` 183 184Using the `benchmarks` target runs a comprehensive set of benchmarks which may 185take some time to run. Subsets of the benchmarks can be run using targets such 186as `make bench-petsc-bp1`, or `make bench-petsc-bp3`. 187 188For more details about the benchmarks, see 189[`benchmarks/README.md`](benchmarks/README.md) 190 191 192## Install 193 194To install libCEED, run 195 196 make install prefix=/usr/local 197 198or (e.g., if creating packages), 199 200 make install prefix=/usr DESTDIR=/packaging/path 201 202Note that along with the library, libCEED installs kernel sources, e.g. OCCA 203kernels are installed in `$prefix/lib/okl`. This allows the OCCA backend to 204build specialized kernels at run-time. In a normal setting, the kernel sources 205will be found automatically (relative to the library file `libceed.so`). 206However, if that fails (e.g. if `libceed.so` is moved), one can copy (cache) the 207kernel sources inside the user OCCA directory, `~/.occa` using 208 209 $(OCCA_DIR)/bin/occa cache ceed $(CEED_DIR)/lib/okl/*.okl 210 211This will allow OCCA to find the sources regardless of the location of the CEED 212library. One may occasionally need to clear the OCCA cache, which can be accomplished 213by removing the `~/.occa` directory or by calling `$(OCCA_DIR)/bin/occa clear -a`. 214 215### pkg-config 216 217In addition to library and header, libCEED provides a [pkg-config][pkg-config1] 218file that can be used to easily compile and link. [For example][pkg-config2], if 219`$prefix` is a standard location or you set the environment variable 220`PKG_CONFIG_PATH`, 221 222 cc `pkg-config --cflags --libs ceed` -o myapp myapp.c 223 224will build `myapp` with libCEED. This can be used with the source or 225installed directories. Most build systems have support for pkg-config. 226 227## Contact 228 229You can reach the libCEED team by emailing [ceed-users@llnl.gov](mailto:ceed-users@llnl.gov) 230or by leaving a comment in the [issue tracker](https://github.com/CEED/libCEED/issues). 231 232## Copyright 233 234The following copyright applies to each file in the CEED software suite, unless 235otherwise stated in the file: 236 237> Copyright (c) 2017, Lawrence Livermore National Security, LLC. Produced at the 238> Lawrence Livermore National Laboratory. LLNL-CODE-734707. All Rights reserved. 239 240See files LICENSE and NOTICE for details. 241 242[ceed-soft]: http://ceed.exascaleproject.org/software/ 243[ecp]: https://exascaleproject.org/exascale-computing-project 244[pkg-config1]: https://en.wikipedia.org/wiki/Pkg-config 245[pkg-config2]: https://people.freedesktop.org/~dbn/pkg-config-guide.html#faq 246
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