intel-mkl

问题 I have a multithreaded code in C, using OpenMP and Intel MKL functions. I have the following code: omp_set_num_threads(nth); #pragma omp parallel for private(l,s) schedule(static) for(l=0;l<lines;l++) { for(s=0;s<samples;s++) { out[l*samples+s]=mkl_ddot(&bands, &hi[s*bands+l], &inc_one, &hi_[s*bands+l], &inc_one); } }//fin for l I want to use all the cores of the multicore processor (the value of nth) in this pramga. But I want that each core computes a single mkl_ddot function independently

问题 I have a multithreaded code in C, using OpenMP and Intel MKL functions. I have the following code: omp_set_num_threads(nth); #pragma omp parallel for private(l,s) schedule(static) for(l=0;l<lines;l++) { for(s=0;s<samples;s++) { out[l*samples+s]=mkl_ddot(&bands, &hi[s*bands+l], &inc_one, &hi_[s*bands+l], &inc_one); } }//fin for l I want to use all the cores of the multicore processor (the value of nth) in this pramga. But I want that each core computes a single mkl_ddot function independently

问题 I try to import skimage.io and have the following error: ...mkl/intel64/libmkl_intel_thread.so: undefined symbol: omp_get_num_procs I have an mkl lib in my LD_LIBRARY_PATH, if I delete it than I have: ImportError: libmkl_rt.so: cannot open shared object file: No such file or directory 回答1: on my workstation, it is the problem with libiomp5.so . You can find the location of the so file with command (i.e. sudo find / -name libiomp5.so on Linux) and add the directory of its location to etc/ld.so

问题 I try to import skimage.io and have the following error: ...mkl/intel64/libmkl_intel_thread.so: undefined symbol: omp_get_num_procs I have an mkl lib in my LD_LIBRARY_PATH, if I delete it than I have: ImportError: libmkl_rt.so: cannot open shared object file: No such file or directory 回答1: on my workstation, it is the problem with libiomp5.so . You can find the location of the so file with command (i.e. sudo find / -name libiomp5.so on Linux) and add the directory of its location to etc/ld.so

问题 I am using mkl cblas_dgemm and currently have it with CblasRowMajor , CblasNoTrans , CblasNotrans , for my matrices. I know that c is a row major language, whereas dgemm is a column major algorithm. I am interested to know if switching the ordering of the matrices will have any affect on the cblas_dgemm algorithm if I am linking against mkl . Is mkl smart enough to do things behind the scenes that I would try to do to optimized the matrix multiplcations? If not, what is the best way to

问题 I am using mkl cblas_dgemm and currently have it with CblasRowMajor , CblasNoTrans , CblasNotrans , for my matrices. I know that c is a row major language, whereas dgemm is a column major algorithm. I am interested to know if switching the ordering of the matrices will have any affect on the cblas_dgemm algorithm if I am linking against mkl . Is mkl smart enough to do things behind the scenes that I would try to do to optimized the matrix multiplcations? If not, what is the best way to

问题 I am working on a project where Intel MKL is nice to have, but not available on all the targeted platforms, so I have to check for its presence to behave accordingly. I have enabled the Intel Performance Libraries in my Visual Studio project's properties, as explained in Compiling and Linking Intel® Math Kernel Library with Microsoft* Visual C++* and in Intel® Math Kernel Library (Intel® MKL) 2018 Getting Started but I'm not getting any of the preprocessor definitions described in Using

问题 I have an AMD cpu and I'm trying to run some code that uses Intel-MKL. The code is significantly slower than I expected. When you have an AMD CPU, can you speed up code that uses the Intel-MKL? How? 回答1: Yes you can speed up your code. Set the environment variable MKL_DEBUG_CPU_TYPE=5 then run your code. FYI this slow down affects anything that uses Intel-MKL library and runs on AMD CPU (i.e. affects all operating systems and affects all programming languages and all programs (older versions

问题 I am using Tensorflow's Anaconda distribution with MKL support. from tensorflow.python.framework import test_util test_util.IsMklEnabled() This code prints True . However, when I compile my Keras model I still get Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 This is not the behavior I was expecting. 回答1: The MKL-DNN portions of the tensorflow execution (which is the main acceleration provided by MKL engineers) are JIT'ed at runtime. So the

问题 I am using Tensorflow's Anaconda distribution with MKL support. from tensorflow.python.framework import test_util test_util.IsMklEnabled() This code prints True . However, when I compile my Keras model I still get Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 This is not the behavior I was expecting. 回答1: The MKL-DNN portions of the tensorflow execution (which is the main acceleration provided by MKL engineers) are JIT'ed at runtime. So the