- LINK SEQUENTIAL PROGRAM WITH MULTITHREAD MKL SERIAL
- LINK SEQUENTIAL PROGRAM WITH MULTITHREAD MKL CODE
The following environment variables are provided for debugging and the scalable memory allocator: Linkage is only possible against shared libraries: icpc -o myprog.exe.
LINK SEQUENTIAL PROGRAM WITH MULTITHREAD MKL CODE
This package can only be used for C++ code for compilation a command of the form icpc -c -o cfoo.o. TBB Usage Compiling and Linking with the TBB Please check the MKL documentation as well as the directory $MKL_BASE/include for available modules and include files. The analogous procedure applies for C interfaces, again illustrated for the DFTI example: Specify #include Īnd compile with icc -c -o cfoo.o. When compiling your code, you then also need to add the include path for the module information file: ifort -c -o foo.o. In the case of DFTI, this would for example be a line of the form use mkl_dfti In this case, it is necessary to write an appropriate module reference into the Fortran source code. The MKL contains functionality encapsulated within Fortran 90 modules (the DFTI API).
If your application uses its own (non-OpenMP) threading, it is recommended that MKL calls run single-threaded: export MKL_SERIAL=yes If MKL should run multi-threaded, please perform the settings export OMP_NUM_THREADS=8 # example to set up for execution with 8 threads
LINK SEQUENTIAL PROGRAM WITH MULTITHREAD MKL SERIAL
If a serial MKL version is loaded (see above), this will also enforce single-threaded execution. If you use the Intel compilers, setting MKL_SERIAL will not be necessary since in this case, the MKL will automatically detect whether it is called from within a parallel region. If you wish to use OpenMP in your own program, but the MKL calls should run single-threaded, please perform the settings export OMP_NUM_THREADS=8 # example to set up for execution with 8 threads 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. LRZ has licensed the performance libraries from Intel for use on the LRZ HPC systems ( Linux Cluster and National Supercomputing System).
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