CPU-implementation of out massively-parallel, generic, MRF MAP solver named mapMAP posing minimal assumptions to the input, allowing rapid solution of a large class of MRF problems.
mapMAP's algorithmic foundation and parallelization concept has been presented at High Performance Graphics 2016 in Dublin, Ireland. For a reprint and further information, please refer to our project page (see below).
Currently, this code implements the following modules and features:
- Customizable (parallel) performance
- Change cost type between float and double
- Templated SIMD width (1, 4, 8 for float; 1, 2, 4 for double)
- Automatically setting SIMD width at compile time
- Supports SSE4/AVX/AVX2, autodetected during build
- Automatically using linear-time optimization for certain submodular cost functions
- Novel linear-time optimization for certain supermodular cost functions
- Two algorithms for parallel tree sampling
- Extensible interfaces for all components, providing user hooks
- Cost functions (unary and pairwise)
- Termination criteria
- Node grouping criteria for the multilevel module
- Choice of two (parallel) coordinate selection algorithms
- Use of heuristics, thereby modifying the solver's structure.
- User hooks for logging intermediate results.
- Solver modules
- Acyclic (BCD) descent
- Spanning tree descent
- Multilevel solving
- Finding and exploiting connected components in the topology
- Test suite for each individual module
What it lacks:
- Capability to process label costs as outlined in the paper
For the license and terms of usage, please see "License, Terms of usage & Reference".
In addition to a "classical", CMake-based workflow, the great folks at tipi.build
contributed (optional) support for their C++ build and dependency manager. With tipi,
building mapmap or using it as a library in your own projects just happens
by a snap of your finger.
- CMake building system (>= 3.0.2)
- C++11 compatible compiler (e.g. gcc-5, MSVC 13, icc 17)
- Intel OneTBB (see Installation instructions for different package managers)
The code has been tested (and compiles without issues) on an Ubuntu 16.04
system with an AVX2-compliant Intel CPU
using gcc/g++ 9.3.0 and Intel OneTBB (2021.5.0). The latter is
licensed under the 3BSD-compatible Apache 2.0 licence (see
ASF legal FAQ).
Please make sure to use an C++11-comptabile compiler and activate the
necessary options.
If you are a Ubuntu user, please click on APT on the OneTBB page linked above.
Google Test will automatically be downloaded and built.
The provided FindTBB.cmake is taken from justusc and licensed under the MIT license.
The following instructions are provided for linux; the Windows workflow should be somewhat similar, though GUI-based.
Step-by-step instructions:
git clone https://github.com/dthuerck/mapmap_cpucd mapmap_cpu && mkdir build && cd build && cmake ..ccmake .and configure the following options (if you want to...):
CMAKE_C_COMPILER- command for your C-compiler, e.g.gcc-5CMAKE_CXX_COMPILER- command for your C++-compiler, e.g.g++-5TBB_DIR- path containingTBBConfig.cmakeBUILD_MEMSAVE- determines if the dynamic programming should allocate memory as needed (ON), saving memory but causing slightly longer execution times or preallocate the whole table (OFF)BUILD_DEMO- decides whether the demo from the wiki is built asmapmap_demoBUILD_TEST- decides whether the test suite is built asmapmap_test
- Configure and generate the Makefile (press
candgfromccmake). - Build the project using
make(ormake -jfor parallel build). - Depending on your configuration, you can now run
mapmap_testand/ormapmap_demo(assuming you activatedBUILD_TESTandBUILD_DEMO).
All prerequisites are provided by tipi.build and the .tipi/deps file, to compile this project simply run :
tipi . -t <platform>
Where platform is either one of linux, windows, macos or any of the supported environments.
mapMAP is implemented as a templated, header only library. A simple
#include "mapmap/full.h"
will do the trick. Remember that in order to work you need to compile your
whole project with C++11 support. All functions and classes are organized
in the namespace mapmap.
For the users of GCC, we recommend the following options for the best performance:
-std=c++11 -Wall -march=native -O2 -flto -mfpmath=sse -funroll-loops
As a good starting point, we recommend studying mapmap_demo.cc closely,
which is mostly self-explanatory.
mapMAP can be easily used with the tipi.build dependency manager, by adding the following to a .tipi/deps:
{
"dthuerck/mapmap_cpu": { }
}For extended documentation on building, using and extending mapMAP, please see the integrated wiki.
An example to start with is available in [example-mapmap_cpu](https://github.com/tipi-deps/example-mapmap_cpu) (change the target name appropriately to `linux` or `macos` or `windows`):
```bash
tipi . -t <target>
Our program is licensed under the liberal BSD 3-Clause license included as LICENSE.txt file.
If you decide to use our code or code based on this project in your application, please make sure to cite our HPG 2016 paper:
@inproceedings{Thuerck2016MRF,
title = {A Fast, Massively Parallel Solver for Large, Irregular Pairwise {M}arkov Random Fields},
author = {Thuerck, Daniel and Waechter, Michael and Widmer, Sven and von Buelow, Max and Seemann, Patrick and Pfetsch, Marc E. and Goesele, Michael},
booktitle = {Proceedings of High Performance Graphics 2016},
year = {2016},
}
A PDF reprint is available here: PDF reprint and PDF Supplementary Material.
For any trouble with building, using or extending this software, please use the project's integrated issue tracker. We'll be happy to help you there or discuss feature requests.
Compared with the development version from our HPG paper (cf. below).
- v1.5 (6/25/2018):
- Added tech report for new tree selection algorithm (from v1.2) in doc/.
- Added novel envelopes for supermodular cost function types ("Antipotts", "LinearPeak"). A tech report may follow.
- Several bugfixes.
- v1.4 (1/10/2018):
- Deterministic solver path with user-provided seed.
- Several bugfixes and smaller improvements.
- v1.3 (10/18/2017):
- Envelope optimization for Potts, TruncatedLinear, TruncatedQuadratic.
- Ability to have individual cost functions per edge.
- Removed UNARY/PAIRWISE template parameters from solver, hiding these internally.
- Improved multilevel performance, even in the case of individual costs.
- Added GTest for automatic built instead of a hard dependency.
- v1.2 (5/29/2017):
- Introduced a new, multicoloring-based tree selection algorithm - lock-free.
- v1.1 (4/12/2017):
- Tuned the tree growing implementation for early termination and
- option for relaxing the maximality requirement.
- v1.0 (2/8/2017):
- Stable release.
- Logging callbacks for use as library.
- Clean, documented interface and documentation.
- Added a demo for correct usage.
- beta (12/6/2016):
- Initial release, mirrors functionality outlined in the paper.
- Automated vectorization (compile-time detection) for float/double.
- Supporting scalar/SSE2-4/AVX/AVX2.
- Added cost function instances.
- Added unit tests.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent