link sequential program with multithread mkl

#Link sequential program with multithread mkl code#

If you wish to use multiprocessing, there are the following possibilities: If linked with parallel support, Intel MKL can make use of shared memory parallelism by default, only a single thread is used. to support the Intel TBB threading model or use the PGI compiler, we suggest using the Intel MKL link-line advisor or the MKL link tool command-line tool (see mkl_link_tool -h for further details). If you need an even more specific linking command for the Intel MKL, e.g. Here CC corresponds to gcc, icc, or mpicc F77 and F90 correspond to gfortran, ifort, mpif77 or mpif90, respectively. If you need optimized BLAS LAPACK or other routines provided by MKL, please provide the library location when linking your executable For special cases and for linking with other compilers you can use the environment variables provided by the intel-mkl module. When you are using an Intel compiler, it is usually sufficient to specify the option -mkl in the compile and link command (see compiler documentation). global/user modulerc -īefore using the MKL please load the environment module intel-mkl: module load intel-mklīefore using the TBB, load the module tbb module load intel-tbbīefore using the IPP, load the module ipp module load intel-ippīefore using the DAAL, load the module daal module load intel-daal The presently installed versions are listed in the following table (default versions are in bold). The Integrated Performance Primitives (IPP) containing highly optimized primitive operations used for digital filtering, audio and image processing, etc. In particular, this package has support for scalable threaded containers (note that the C++ Standard Template Library, STL, is typically not thread-safe). Threading Building Blocks (TBB), which enable the C++ programmer to relatively easily integrate (shared-memory) parallel capability into her/his code. ScaLAPACK and distributed FFT implementations for various MPI flavors. Math Kernel Libraries (MKL), containing well-optimized implementations of the BLAS and LAPACK interfaces, sparse solvers, support for interval arithmetic, FFT routines, and other functionality. LLIBS = -L/software/matlab/R2011a/bin/glnxa64 -leng -lmat -lmx -lut -Wl,-start-group $/lib/intel64/libmkl_core.LRZ has licensed the performance libraries from Intel for use on the LRZ HPC systems ( Linux Cluster and National Supercomputing System).

#Link sequential program with multithread mkl code#

In the makefile there is 'sequential' in LLIBS, but there are sections of the code that are already multithreading with OpenMP, only do-loops and no explicit MKL routines. My OS is then Linux, the compiler is iFort/intel fortran, linking is dynamic(unsure of this, trying static yields same result/error), the interface is LP64.įor threading layer Im uncertain of which option to select, sequential or OpenMP.

With the advisor,I choose the 'Composer XE 2011' in the link advisor since that is where my MKL roots are in the makefile(please correct me if this is a wrong assumption), MKLroot=/software/intel/parallel_studio/composer_xe_2011_sp1.7.256/mkl/lib/intel64

This is done right before I call pardiso and has no effect on the number of cores used, the next step was to check the MKL linking, I then tried to use the link advisor, but I get and error. The simplest fix call mkl_set_dynamic(0) ! disable adjustment of the number of threads The problem is 'large' with #equations: 71574 Iparm(8) = 9 ! Iterative refinement step. When calling pardiso I use the following iparm Question: How do I get pardiso to work with multiple cores? I have tried some 'easy' fixes that didn't work, then I tried the link advisor and get an error when linking. Hello, I'm currently trying to get pardiso to work with multi threading and I'm wondering if it is a linking issue or something else.