A library for the creation of 2:1 balanced octrees. Implements a parallel and a serial bottom-up algorithm. The parallel algorithm is a modified version of Bottom-up construction and 2: 1 balance refinement of linear octrees in parallel by Sundar et al.
Its optimized for shared memory systems instead of distributed memory systems, using OpenMP and Arch D. Robisons parallel stable sort algorithm for OpenMP.
Requires:
Optional:
- tcmalloc (for better performance)
- googletest master (for building the tests)
- hayai v1.0.1 (for building the benchmarks)
- gperftools (for profiling)
- doxygen (for generating the documentatíon).
In the project root run:
mkdir build
cd build
cmake ..
make
To install the library at the location defined by the CMAKE_INSTALL_PREFIX variable run make install inside the build directory.
Set BUILD_SHARED_LIBS=ON to build a shared library (static by default).
Run make doc to generate the documentation in <project-root>/doc (doxygen must be installed and and found by cmake).
Clone googletest into <project-root>/googletest and set the cmake variable BUILD_TESTS=ON:
git clone https://github.com/google/googletest.git
cd build
cmake .. -DBUILD_TESTS=ON
./octreebuilder/tests/octreebuilder-unittests
./octreebuilder/tests/octreebuilder-integrationtests
Install hayai, set the cmake variable BUILD_BENCHMARKS=ON then run the benchmark executable.
#include <iostream>
#include <memory>
#include <random>
#include <omp.h>
#include <octreebuilder/paralleloctreebuilder.h>
#include <octreebuilder/sequentialoctreebuilder.h>
#include <octreebuilder/octree.h>
using namespace octreebuilder;
int main(int, char**) {
const Vector3i maxCoord(1000, 1000, 1000);
std::unique_ptr<OctreeBuilder> octreeBuilder;
if (omp_get_max_threads() > 1) {
octreeBuilder = std::unique_ptr<OctreeBuilder>(new ParallelOctreeBuilder(maxCoord));
} else {
octreeBuilder = std::unique_ptr<OctreeBuilder>(new SequentialOctreeBuilder(maxCoord));
}
std::default_random_engine generator;
std::uniform_int_distribution<coord_t> coordinateDistribution(0, 1000);
auto genCoord = std::bind(coordinateDistribution, generator);
for (size_t i = 0; i < 5000; i++) {
octreeBuilder->addLevelZeroLeaf(Vector3i(genCoord(), genCoord(), genCoord()));
}
std::unique_ptr<Octree> octree = octreeBuilder->finishBuilding();
std::cout << "Octree: " << octree << std::endl;
}
Output:
Octree: { depth: 10, maxLevel: 5, maxXYZ: (1023, 1023, 1023), numNodes: 1693581 }
Install TCMalloc to speed up memory allocation.
On a ubuntu system either install gperftools (see below) or the standalone version of TCMalloc (sudo apt-get install libtcmalloc-minimal4).
The library will automatically link against tcmalloc if it is found.
If you don't want this set the cmake flag BUILD_WITH_TCMALLOC=OFF.
Streaming SIMD Extensions (SSE) are used by default to speed up vector calculations.
If your system does not support SSE2 and SSE3 set the flag BUILD_WITH_SSE=OFF.
Install gperftools (e.g. on ubuntu systems run sudo apt-get install libgoogle-perftools-dev),
set the cmake variable ENABLE_PROFILING=ON and build in release mode with debug information.
Run the benchmark executable (should create OctreeBuilderBenchmark.prof), create a callgrind file with pprof and analyze the results with kcachegrind:
cd build
cmake .. -DCMAKE_BUILD_TYPE=RelWithDebInfo -DBUILD_BENCHMARKS=ON -DENABLE_PROFILING=ON
cd benchmarks
./benchmarks
google-pprof benchmarks OctreeBuilderBenchmark.prof --callgrind > bench.callgrind
kcachegrind bench.callgrind