intel-mkl

问题 I wanted to test some signal processing and statistics using SciPy. So I had to use scipy.signal and scipy.stats , but I always used to get an error: ImportError: DLL load failed: The specified module could not be found. I was using Numpy 1.7.1, scipy 0.12 and Python 2.7.3. I checked on the internet and asked about it on other forums too! This problem got solved when switched my Numpy distribution with the Numpy-MKL distribution. I want to know the difference between the two libraries ? 回答1:

问题 I wanted to test some signal processing and statistics using SciPy. So I had to use scipy.signal and scipy.stats , but I always used to get an error: ImportError: DLL load failed: The specified module could not be found. I was using Numpy 1.7.1, scipy 0.12 and Python 2.7.3. I checked on the internet and asked about it on other forums too! This problem got solved when switched my Numpy distribution with the Numpy-MKL distribution. I want to know the difference between the two libraries ? 回答1:

问题 I use the LAPACK zheev routine in a Fortran scientific code to compute both eigenvalues and vectors of a matrix not too big (likely to never exceed size 1000). Since this step happens at the beginning of the computation I have to achieve a great accuracy to avoid important error propagation. The problem is that the accuracy of the computation is only about 1e-9 in my test case (with a 12x12 matrix only) and that is not enough at all. I compared with numpy.linalg.eigh which give ridiculously

问题 This is a follow-up question to this other with the same title (I did a major edit to it, but I was told it should be another question - and I can't think of another title). I am using Intel's MKL LAPACKE and CBLAS to calculate yn = trans(a)*inv(zt)*a + trans(b)*inv(zl)*b Where a and b are m-by-n real matrices, zt and zl are m-by-m complex matrices. The resulting complex matrix yn is n-by-n. Here is how I am doing it: zt <- inv(zt) zl <- inv(zl) c <- zt*a yn <- trans(a)*c c <- zl*b yn <-

问题 I am porting a Matlab algorithm with lots of coefficient-wise array operations to C++, which look like this example, but are often much more complex: Eigen::Array<double, Dynamic, 1> tx2(12); tx2 << 1,2,3,4,5,6; Eigen::Array<double, Dynamic, 1> tx1(12); tx1 << 7,8,9,10,11,12; Eigen::Array<double, Dynamic, 1> x = (tx1 + tx2) / 2; The C++ code turned out to be significantly slower than Matlab (around 20%). So in a next step I tried to turn on the Intel MKL implementation of Eigen, which did

问题 I am trying to compute 3D convolution of a 3D array using Intel MKL . Could someone kindly give me some hints how I can do that? Is it achievable using MKL ? Thanks in advance. 回答1: Intel has an example on their page of a 3D FFT, which should be helpful for performing convolution by multiplication in frequency space. Sorry I don't have a full solution: Three-Dimensional REAL FFT (C Interface) #include "mkl_dfti.h" float x[32][100][19]; float _Complex y[32][100][10]; /* 10 = 19/2 + 1 */ DFTI

问题 I installed Tensorflow with GPU support and Keras to an environment in Anaconda (v1.6.5) by using following commands: conda install -n EnvName tensorflow-gpu conda install -n EnvName -c conda-forge keras-gpu I have NVIDIA Quadro 2200K on my machine with driver v384.66, cuda-8.0, cudnn 7.0 When I am trying to run a python code with Keras at the stage of training I get the following Intel MKL ERROR: Parameter 4 was incorrect on entry to DLASCL. and later File "/home/User/anaconda3/envs/keras

问题 Fast Fourier Transforms (FFTs) in Numpy/SciPy are not threaded. Enthought Python is shipped with the Intel MKL numerical library, which is capable of threaded FFTs. How does one get access to these routines? 回答1: The following code works for me with Enthought 7.3-1 (64-bit) on Windows 7 Ultimate 64-bit. I haven't benchmarked it but it certainly uses all cores at once rather than just one. from ctypes import * class Mkl_Fft: c_double_p = POINTER(c_double) def __init__(self,num_threads=8): self

问题 I wrote a script using andaconda2 python2.7, and wxpython, matplotlib, skimage, numpy. After using pyinstaller to generate an executable files. the total size is almost 700 mb. it feels too large. Someone said because numpy uses MKL which is very large ~ 400 mb as I saw in folder. so I wonder how to create an environment using numpy nomkl? Or if anyone has experience to reduce the size of executable files using pyinstaller, please let me know. BTW, I tried py2exe. it creates a much smaller

问题 Do anyone have any idea how can I rewrite eig(A,B) from Matlab used to calculate generalized eigenvector/eigenvalues? I've been struggling with this problem lately. So far: Matlab definition of eig function I need: [V,D] = eig(A,B) produces a diagonal matrix D of generalized eigenvalues and a full matrix V whose columns are the corresponding eigenvectors so that A*V = B*V*D. So far I tried the Eigen library (http://eigen.tuxfamily.org/dox/classEigen_1_1GeneralizedSelfAdjointEigenSolver.html)