namespace Eigen { /** \page TopicMultiThreading Eigen and multi-threading \section TopicMultiThreading_MakingEigenMT Make Eigen run in parallel Some %Eigen's algorithms can exploit the multiple cores present in your hardware. To this end, it is enough to enable OpenMP on your compiler, for instance: - GCC: \c -fopenmp - ICC: \c -openmp - MSVC: check the respective option in the build properties. You can control the number of threads that will be used using either the OpenMP API or %Eigen's API using the following priority: \code OMP_NUM_THREADS=n ./my_program omp_set_num_threads(n); Eigen::setNbThreads(n); \endcode Unless `setNbThreads` has been called, %Eigen uses the number of threads specified by OpenMP. You can restore this behavior by calling `setNbThreads(0);`. You can query the number of threads that will be used with: \code n = Eigen::nbThreads( ); \endcode You can disable %Eigen's multi threading at compile time by defining the \link TopicPreprocessorDirectivesPerformance EIGEN_DONT_PARALLELIZE \endlink preprocessor token. Currently, the following algorithms can make use of multi-threading: - general dense matrix - matrix products - PartialPivLU - row-major-sparse * dense vector/matrix products - ConjugateGradient with \c Lower|Upper as the \c UpLo template parameter. - BiCGSTAB with a row-major sparse matrix format. - LeastSquaresConjugateGradient \warning On most OS it is <strong>very important</strong> to limit the number of threads to the number of physical cores, otherwise significant slowdowns are expected, especially for operations involving dense matrices. Indeed, the principle of hyper-threading is to run multiple threads (in most cases 2) on a single core in an interleaved manner. However, %Eigen's matrix-matrix product kernel is fully optimized and already exploits nearly 100% of the CPU capacity. Consequently, there is no room for running multiple such threads on a single core, and the performance would drops significantly because of cache pollution and other sources of overheads. At this stage of reading you're probably wondering why %Eigen does not limit itself to the number of physical cores? This is simply because OpenMP does not allow to know the number of physical cores, and thus %Eigen will launch as many threads as <i>cores</i> reported by OpenMP. \section TopicMultiThreading_UsingEigenWithMT Using Eigen in a multi-threaded application In the case your own application is multithreaded, and multiple threads make calls to %Eigen, then you have to initialize %Eigen by calling the following routine \b before creating the threads: \code #include <Eigen/Core> int main(int argc, char** argv) { Eigen::initParallel(); ... } \endcode \note With %Eigen 3.3, and a fully C++11 compliant compiler (i.e., <a href="http://en.cppreference.com/w/cpp/language/storage_duration#Static_local_variables">thread-safe static local variable initialization</a>), then calling \c initParallel() is optional. \warning Note that all functions generating random matrices are \b not re-entrant nor thread-safe. Those include DenseBase::Random(), and DenseBase::setRandom() despite a call to `Eigen::initParallel()`. This is because these functions are based on `std::rand` which is not re-entrant. For thread-safe random generator, we recommend the use of c++11 random generators (\link DenseBase::NullaryExpr(Index, const CustomNullaryOp&) example \endlink) or `boost::random`. In the case your application is parallelized with OpenMP, you might want to disable %Eigen's own parallelization as detailed in the previous section. \warning Using OpenMP with custom scalar types that might throw exceptions can lead to unexpected behaviour in the event of throwing. */ }