zfp is an open source C/C++ library for compressed numerical arrays that support high throughput read and write random access. zfp also supports streaming compression of integer and floating-point data, e.g., for applications that read and write large data sets to and from disk. zfp is primarily written in C and C++ but also includes Python and Fortran bindings.
zfp was developed at Lawrence Livermore National Laboratory and is loosely based on the algorithm described in the following paper:
Peter Lindstrom
"Fixed-Rate Compressed Floating-Point Arrays"
IEEE Transactions on Visualization and Computer Graphics
20(12):2674-2683, December 2014
doi:10.1109/TVCG.2014.2346458
zfp was originally designed for floating-point arrays only, but has been extended to also support integer data and could for instance be used to compress images and quantized volumetric data. To achieve high compression ratios, zfp generally uses lossy but optionally error-bounded compression. Bit-for-bit lossless compression is also possible through one of zfp's compression modes.
zfp works best for 2D and 3D arrays that exhibit spatial correlation, such as continuous fields from physics simulations, images, regularly sampled terrain surfaces, etc. Although zfp also provides a 1D array class that can be used for 1D signals such as audio, or even unstructured floating-point streams, the compression scheme has not been well optimized for this use case, and rate and quality may not be competitive with floating-point compressors designed specifically for 1D streams. zfp also supports compression of 4D arrays.
zfp is freely available as open source under a BSD license, as outlined in the file 'LICENSE'. For more information on zfp and comparisons with other compressors, please see the zfp website. For bug reports, please consult the GitHub issue tracker. For questions, comments, and requests, please send e-mail to zfp@llnl.gov.
Full documentation is available online via Read the Docs. A PDF version is also available.
zfp consists of three distinct parts: a compression library written in C; a set of C++ header files with C wrappers that implement compressed arrays; and a set of C and C++ examples. The main compression codec is written in C and should conform to both the ISO C89 and C99 standards. The C++ array classes are implemented entirely in header files and can be included as is, but since they call the compression library, applications must link with libzfp.
On Linux, macOS, and MinGW, zfp is easiest compiled using gcc and gmake. CMake support is also available, e.g., for Windows builds. See below for instructions on GNU and CMake builds.
zfp has successfully been built and tested using these compilers:
gcc versions 4.4.7, 4.9.4, 5.5.0, 6.1.0, 6.4.0, 7.1.0, 7.3.0, 8.1.0
icc versions 15.0.6, 16.0.4, 17.0.2, 18.0.2, 19.0.0
clang versions 3.9.1, 4.0.0, 5.0.0, 6.0.0
MinGW version 5.3.0
Visual Studio versions 14 (2015), 15 (2017)
zfp conforms to various language standards, including C89, C99, C11, C++98, C++11, and C++14.
NOTE: zfp requires 64-bit compiler and operating system support.
To build zfp using gcc, type
make
from this directory. This builds libzfp as a static library as well as utilities and example programs. See documentation for complete build instructions.
To build zfp using CMake on Linux or macOS, start a Unix shell and type
mkdir build
cd build
cmake ..
make
To also build the examples, replace the cmake line with
cmake -DBUILD_EXAMPLES=ON ..
To build zfp using Visual Studio on Windows, start a DOS shell, cd to the top-level zfp directory, and type
mkdir build
cd build
cmake ..
cmake --build . --config Release
This builds zfp in release mode. Replace 'Release' with 'Debug' to build zfp in debug mode. See the instructions for Linux on how to change the cmake line to also build the example programs.
To test that zfp is working properly, type
make test
or using CMake
ctest
If the compilation or regression tests fail, it is possible that some of the macros in the file 'Config' have to be adjusted. Also, the tests may fail due to minute differences in the computed floating-point fields being compressed, which will be indicated by checksum errors. If most tests succeed and the failures result in byte sizes and error values reasonably close to the expected values, then it is likely that the compressor is working correctly.