Platform is Ubuntu 16.04.5. GPU NVIDIA1070 cuda-9.2 python3.5

I use python with nvblas support by compiling numpy against intel MKL. It works with 2018.1 and 2018.2 but GPU is not used for MKL 2018.3 and 2019.0

Here is the experiment (have tried with different versions of numpy same issue)

LD_PRELOAD=/usr/local/cuda/targets/x86_64-linux/lib/libnvblas.so python3

NVBLAS_CONFIG_FILE environment variable is set to '/home/bernard/.config/nvblas.conf'

Python 3.5.2 (default, Nov 23 2017, 16:37:01)

[GCC 5.4.0 20160609] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>> import numpy as np

>>> np.__version__ '1.15.2'

>>> np.show_config()

lapack_opt_info: define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

library_dirs = ['/opt/intel/mkl/lib/intel64/']

include_dirs = ['/opt/intel/mkl/include']

libraries = ['mkl_rt', 'pthread'] blas_mkl_info: define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

library_dirs = ['/opt/intel/mkl/lib/intel64/']

include_dirs = ['/opt/intel/mkl/include']

libraries = ['mkl_rt', 'pthread']

lapack_mkl_info: define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

library_dirs = ['/opt/intel/mkl/lib/intel64/']

include_dirs = ['/opt/intel/mkl/include']

libraries = ['mkl_rt', 'pthread'] blas_opt_info: define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]

library_dirs = ['/opt/intel/mkl/lib/intel64/']

include_dirs = ['/opt/intel/mkl/include'] libraries = ['mkl_rt', 'pthread']

>>> a = np.random.rand(10000, 10000)

>>> b = np.random.rand(10000, 10000)

>>> a@b

now check with the command: nvidia-smi

For MKL <= 2018.3 you can see the gpu is being used 100%, with MKL > 2018.3 it is 0% so the gpu is not used,

From the LAPACK's FAQ, we know it does not support sparse matrices:

Just to take a chance, does intel MKL break this restriction by introducing sparse matrices in LAPACK? If not, is there any other function domain of MKL that uses sparse matrices? Thanks.

I'm attempting to compute an FFT on sizes N>=2048. What I can't explain is why the FFT output looks great for N<2048 but then goes to garbage when N>=2048. I've poured over other forum posts and examples online and haven't found anything useful to date. I'm using the 2019 initial release on 64-bit CentOS7 machine compiled into C++ project.

My code is pretty simple, but hopefully it is something obvious that I've just overlooked.

My tempSignal variable is the real data coming from an RF receiver. You'll notice the printf statements. The first printf statement is logging the fftInput to terminal which I then copy into Matlab. The final printf shows only the first 20 entries of the fftOutput which I then compare to what Matlab produces via its fft function.

unsigned int N = 2048
std::vector<MKL_Complex8> fftInput(N);
for(unsigned int i=0; i<fftInput.size(); ++i) {
  fftInput.data()[i].real = tempSignal.getData()[i].real();
  fftInput.data()[i].imag = tempSignal.getData()[i].imag();
  printf("%f + %fi\n", fftInput.data()[i].real, fftInput.data()[i].imag);
}

std::vector<MKL_Complex8> fftOutput(fftInput.size());

DFTI_DESCRIPTOR_HANDLE descriptor;
MKL_LONG status;
status = DftiCreateDescriptor(&descriptor, DFTI_SINGLE, DFTI_COMPLEX, 1, fftInput.size());
status = DftiSetValue(descriptor, DFTI_PLACEMENT, DFTI_NOT_INPLACE); //Out of place FFT
status = DftiCommitDescriptor(descriptor); //Finalize the descriptor
status = DftiComputeForward(descriptor, fftInput.data(), fftOutput.data()); //Compute the Forward FFT
status = DftiFreeDescriptor(&descriptor); //Free the descriptor

for(unsigned int i=0; i<20; ++i) {
  printf("i: %d, (%f, %f)\n", i, fftOutput[i].real, fftOutput[i].imag);
}

Thanks in advance for any help.

Is there any documentation or explanation for MKL_DIRECT_JIT apart from a one-line mention in the release notes?

Hello,

Per https://software.intel.com/en-us/mkl-developer-reference-c-sparse-blas-c... a matrix stored in CSR storage format should contain only non-zero values.  

We've ran into a case where the result of mkl_sparse_spmm contains some zero values. Is this expected? I can provide a repro if needed.

Thanks,

Sid

I am new to Fortran and I am comparing ifort's speed to Julia. I would greatly appreciate any help or insight into my issues and taking the time to answer some of my questions. 

When running what appears to be similar code, calling LAPACK to diagonalize a large matrix, Julia/OpenBLAS blasts all 4 cores of my laptop (intel i5), while ifort/MKL only use 2 of the 4. If I use the MKL_NUM_THREADS and MKL_DYNAMIC variables, I can force ifort/MKL to use all 4 cores, but the performance actually goes down a little. Even though only 2 cores are running, Fortran still beats Julia/OpenBLAS (not by much though). 

Questions: I am just naively watching the CPU activity in the Gnome system monitor. I understand it is possible the Lanczos routines may not be suited for parallel work, but why are 2 cores being used? I would expect either 1 or all cores. Is the CPU doing everything it can for the calculation? What is happening when I set MKL_DYANMIC=false? Is data just being sloshed around the cores in a slow manner, making the cores stay at "100%" but not advancing the calculation?

Side note: Is is expected for MKL_DYNAMIC and other env variables to be undefined, even after sourcing ~/intel/parallel_studio_xe_2019.0.045/psxevars.sh?

Other info: This is on a fresh install of Ubuntu 18.04. I also had to set ulimit -s unlimited to avoid segfaults. The Parallel studio GUI install complained about 32bit libraries, installing libc6-dev-i386 got rid of the warnings. Julia is the standard v1.0.1 build as they describe on their github. 

Below are the examples, the Julia eigen() function boils down to calling ?geev(x). 

Fortran: compiled with ifort -mkl my_test.f90 

program my_test
      implicit none
      integer::n,info,lwork
      real(8) :: test(1)
      real(8), allocatable:: M(:,:),ansV(:,:),ansE(:),work(:)
      n = 10000
      allocate(M(1:n,1:n))
      allocate(ansV(1:n,1:n))
      allocate(ansE(1:n))
      ! this first call is just to get the ideal workspace size
      ! lwork = -1, so dsyev figures out the workspace size (contained in test(1)) and exits
      call dsyev('V','U',n,ansV,n,ansE,test,-1,info)
      lwork = int(test(1))
      allocate(work(1:lwork))
      call random_number(M)
      M = M + transpose(M)
      ansV = M
      call dsyev('V','U',n,ansV,n,ansE,work,lwork,info)
      print*,ansE
      print*,lwork
      print*,info
      deallocate(M)
      deallocate(ansV)
      deallocate(ansE)
      deallocate(work)
end program my_test
Julia: it is called via julia my_test.jl

using LinearAlgebra
mat = rand(10000,10000)
mat = mat + transpose(mat)
u,v = eigen(mat)
print(u)

Intel® Math Kernel Library (Intel® MKL) is a highly optimized, extensively threaded, and thread-safe library of mathematical functions for engineering, scientific, and financial applications that require maximum performance.

Intel MKL 2018 Update 4 packages are now ready for download.

Intel MKL is available as part of the Intel® Parallel Studio XE and Intel® System Studio. Please visit the Intel® Math Kernel Library Product Page.

Please see What's new in Intel MKL 2018 and in MKL 2018 Update 4 follow this link - https://software.intel.com/en-us/articles/intel-math-kernel-library-release-notes-and-new-features

and here is the link to the MKL 2018 Bug Fix list - https://software.intel.com/en-us/articles/intel-math-kernel-library-inte...

Hi there,

I am running some generalized eigenvalue buckling problems (Kg.v = (1/λ).Ke.v) in MKL and in most cases getting an internal error. I'm using this C# Library and coding in VB.NET. I have ruled out that the issue is from the matrices or from the CSparse-Interop library and narrowed it down to the mkl_sparse_d_gv function.

Below is an example problem. Any insights you can offer would be appreciated.

'Ke is Symmetric and Positive Definite
Dim Ke = New Double(92, 92) {{233496.515545615, 340, 0, 0, 0, -690489.546636846, 111748.257772808, -510, -85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {340, 517.037436845799, 0, 0, 0, -510, -85, -1521.17081958177, 243.547972945083, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 1034.0748736916, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 510, -85, 1521.17081958177, 243.547972945083, -510, -84.9999999999999, -1521.17081958177, 243.547972945084, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 233496.515545615, 340, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 690489.546636846, 111748.257772808, 510, -85}, {0, 0, 0, 340, 517.037436845799, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 510, -85, 1521.17081958177, 243.547972945083}, {-690489.546636846, -510, 0, 0, 0, 5923916.37309477, 0, 24480, 0, -2961958.18654739, 690489.546636846, -12240, 510, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {111748.257772808, -85, 0, 0, 0, 0, 466993.031091231, 0, 680, -690489.546636846, 111748.257772808, -510, -85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {-510, -1521.17081958177, 0, 0, 0, 24480, 0, 13367.0261948794, 0, -12240, 510, -6683.51309743971, 1521.17081958177, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {-85, 243.547972945083, 0, 0, 0, 0, 680, 0, 1034.0748736916, -510, -85, -1521.17081958177, 243.547972945083, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, -2961958.18654739, -690489.546636846, -12240, -510, 5923916.37309477, 0, 24480, 0, -2961958.18654739, 690489.546636846, -12240, 510, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 690489.546636846, 111748.257772808, 510, -85, 0, 466993.031091231, 0, 680, -690489.546636846, 111748.257772808, -510, -85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, -12240, -510, -6683.51309743971, -1521.17081958177, 24480, 0, 13367.0261948794, 0, -12240, 510, -6683.51309743971, 1521.17081958177, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 510, -85, 1521.17081958177, 243.547972945083, 0, 680, 0, 1034.0748736916, -510, -85, -1521.17081958177, 243.547972945083, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, -2961958.18654739, -690489.546636846, -12240, -510, 5923916.37309477, 0, 24480, 0, -2961958.18654739, 690489.546636846, -12240, 510, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 690489.546636846, 111748.257772808, 510, -85, 0, 466993.031091231, 0, 680, -690489.546636846, 111748.257772808, -510, -85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, -12240, -510, -6683.51309743971, -1521.17081958177, 24480, 0, 13367.0261948794, 0, -12240, 510, -6683.51309743971, 1521.17081958177, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 510, -85, 1521.17081958177, 243.547972945083, 0, 680, 0, 1034.0748736916, -510, -85, -1521.17081958177, 243.547972945083, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2961958.18654739, -690489.546636846, -12240, -510, 5923916.37309477, 0, 24480, 0, -2961958.18654739, 690489.546636846, -12240, 510, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 690489.546636846, 111748.257772808, 510, -85, 0, 466993.031091231, 0, 680, -690489.546636846, 111748.257772808, -510, -85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -12240, -510, -6683.51309743971, -1521.17081958177, 24480, 0, 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'Kg is Symmetric
Dim Kg = New Double(92, 92) {{0, 18.1547619047622, 0, 0, 0, 0, 0, -197.619047619051, -0.595238095238095, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {18.1547619047622, 0.0607142857142857, 0, 0, 0, -1.78571428571429, -17.2619047619051, 0.394642857142857, -0.0455357142857143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0.121428571428571, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -448.214285714307, 45.2380952380982, -0.394642857142857, -0.0455357142857143, 448.214285714306, 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Hallo,

I would like to use mkl_sparse_z_syprd in python
https://software.intel.com/en-us/mkl-developer-reference-c-mkl-sparse-syprd

In comparison with the older functions which require indices and data of a sparse matrix, this one requires "Handle containing a sparse matrix in the internal data structure". The Handle could be created with mkl_sparse_?_create_csr
https://software.intel.com/en-us/mkl-developer-reference-c-mkl-sparse-cr...

but it by itself has an empty Holder sparse_matrix_t *A as a parameter. 

Are there any examples of how to implement this operation in python? Particularly how to create an empty sparse matrix holder. A good example of how to use mkl in python is given here
https://software.intel.com/en-us/articles/using-intel-mkl-in-your-python...

Thanks

Is there a way to request a new feature to change the maximum number of CGS iterations in the hybrid Preconditioned CGS/CG solve in Pardiso? iparm[3] allows you to change the convergence tolerance and a factorization option. I do not think it it possible to change the maximum number of iterations to meet the convergence criterion. I think this could be done in iparm[3] by doing:

iparm[3]= 1000*MaxIts+10*L+K,

rather than its current form

iparm[3]= 10*L+K.

Seems easy?

Hello,

I am using NumPy/SciPy manually built with MKL as BLAS. I am wondering is it possible to update from MKL 2018 update 3 to MKL 2019 without rebuilding NumPy/SciPy?

Thanks in advance for providing information!

Hi,

I see in the documentation that Intels' Poisson solver routines can be used to solve equations of the form

∇.∇u = f

Can they also (perhaps via some additional steps) be applied to an equation of the form

∇.ε∇u = f

Thanks

Hi,

I've recently downloaded the intel MKL, but am unable to locate example files for PARDISO. I've tried looking in areas suggested here and here , and have tried running commands like

find $MKLROOT -iname "*solve*"

But no luck.

Are examples available online? Or is there a particular download option I must select to obtain the examples?

Thanks

Dear all,

I see the following terminal output when using pdsyevx from Intel MKL 2019.0.117 with mpich 3.2.1 to calculate only some eigenvalues:

{    0,    0}:  On entry to 
PDORMTR parameter number   12 had an illegal value 
{    0,    0}:  On entry to 
PDORMTR parameter number   12 had an illegal value 
{    0,    0}:  On entry to 
PDORMTR parameter number   12 had an illegal value 

whereas no warnings in given when using openblas 0.3.3 + netlib-scalapack 2.0.2 + openmpi 3.1.2.  For both setups GCC 7.3.0 from Ubuntu 18.04.1 LTS is used.

Note that I do NOT call pdormtr manually, it must be called from within pdsyevx. It looks like its implementation in Intel MKL has some issues for this case.

Below is a MWE that demonstrates the problem.

#include <mpi.h>
#include <iostream>
#include <iomanip>
#include <string>
#include <fstream>
#include <iostream>
#include <stdlib.h>
#include <sstream>
#include <cmath>
#include <vector>
#include <functional>
using namespace std;

// test program to compute last 5 eigenvalues without eigenvectors for symmetric operators
//
// compile with:
// mpic++ -std=c++11 example.cc -o example -lscalapack -lopenblas
// mpic++ -std=c++11 example.cc -o example -lmkl_scalapack_lp64 -lmkl_blacs_intelmpi_lp64 -lmkl_intel_lp64 -lmkl_core -lmkl_sequential


extern "C" {
  /* Cblacs declarations */
  void Cblacs_pinfo(int *, int *);
  void Cblacs_get(int, int, int *);
  void Cblacs_gridinit(int *, const char *, int, int);
  void Cblacs_gridinfo(int, int *, int *, int *,int *);
  void Cblacs_pcoord(int, int, int *, int *);
  void Cblacs_gridexit(int);
  void Cblacs_barrier(int, const char *);
  void Cdgerv2d(int, int, int, double *, int, int, int);
  void Cdgesd2d(int, int, int, double *, int, int, int);

  int numroc_ (const int *n, const int *nb, const int *iproc, const int *isproc, const int *nprocs);

  void pdpotrf_(const char *UPLO,
                const int *N,
                double *A, const int *IA, const int *JA, const int *DESCA,
                int *INFO);

  void descinit_ (int *desc, const int *m, const int *n, const int *mb, const int *nb, const int *irsrc, const int *icsrc, const int *ictxt, const int *lld, int *info);

  int indxl2g_ (const int *indxloc, const int *nb, const int *iproc, const int *isrcproc, const int *nprocs);

  void pdpotri_(const char *UPLO,
                const int *N,
                double *A, const int *IA, const int *JA, const int *DESCA,
                int *INFO);

  void
  pdsyevx_(const char *  jobz,
           const char *  range,
           const char *  uplo,
           const int *   n,
           double *      A,
           const int *   ia,
           const int *   ja,
           const int *   desca,
           const double *VL,
           const double *VU,
           const int *   il,
           const int *   iu,
           const double *abstol,
           const int *   m,
           const int *   nz,
           double *      w,
           double *      orfac,
           double *      Z,
           const int *   iz,
           const int *   jz,
           const int *   descz,
           double *      work,
           int *         lwork,
           int *         iwork,
           int *         liwork,
           int *         ifail,
           int *         iclustr,
           double *      gap,
            int * info);

    double
    pdlamch_(const int *ictxt, const char *cmach);
}

int main(int argc, char **argv)
{
  int mpirank,nprocs;
  MPI_Init(&argc, &argv);
  MPI_Comm_rank(MPI_COMM_WORLD, &mpirank);
  MPI_Comm_size(MPI_COMM_WORLD, &nprocs);
  const char uplo('L');
  const int first_process_row(0);
  const int first_process_column(0);

  const bool mpiroot = (mpirank == 0);

  /* Scalapack / Blacs Vars */
  const int N = 200, Nb = 32;
  int M = N, Mb = Nb;

  if (mpiroot)
    cout << "Running with N="<< N << " Nb="<< Nb << std::endl;

  int procrows = 0, proccols = 0;
  {
    const int n_processes_heuristic = int(std::ceil((1.*N)/Nb))*
                                      int(std::ceil((1.*M)/Mb));
    const int Np = std::min(n_processes_heuristic, nprocs);
    const double ratio = 1.0;
    const int Pc = std::sqrt(ratio * Np);
    proccols = std::min (Np, std::max(2, Pc));
    procrows = Np / proccols ;
  }

  /* Begin Cblas context */
  int ctxt = 0, myrow = 0, mycol = 0, numproc = nprocs;
  // Cblacs_pinfo(&myid, &numproc);
  Cblacs_get(0, 0, &ctxt);
  Cblacs_gridinit(&ctxt, "Row-major", procrows, proccols);
  Cblacs_gridinfo( ctxt, &procrows, &proccols, &myrow, &mycol );

  if (mpiroot)
    cout << "Process grid "<< procrows << "x"<<proccols << "<="<< numproc << std::endl;

  if ((myrow > -1) && (mycol > -1))
    {
      if (mpiroot)
        cout << "setup..."<< std::endl;

      // we won't need eigenvectors, use a sufficiently small distributed matrix on the same process grid:
      const int N_B = procrows;
      const int M_B = procrows;
      const int Nb_B = 1;
      const int Mb_B = 1;

      const int nrows_B = numroc_(&N_B, &Nb_B, &myrow, &first_process_row, &procrows);
      const int ncols_B = numroc_(&M_B, &Mb_B, &mycol, &first_process_column, &proccols);
      const int lda_B = max(1,nrows_B);

      int info=0;
      int descB[9];
      descinit_(descB, &N_B, &M_B, &Nb_B, &Mb_B,&first_process_row,&first_process_column,&ctxt, &lda_B, &info);
      if (info != 0)
        cerr << "error in descinit_"<< std::endl;

      std::vector<double> B(nrows_B*ncols_B);

      // set the matrix:
      const int nrows = numroc_(&N, &Nb, &myrow, &first_process_row, &procrows);
      const int ncols = numroc_(&M, &Mb, &mycol, &first_process_column, &proccols);

      const int lda = max(1,nrows);
      int descA[9];
      descinit_(descA, &N, &M, &Nb, &Mb,&first_process_row,&first_process_column,&ctxt, &lda, &info);
      if (info != 0)
        cerr << "error in descinit_"<< std::endl;

      std::vector<double> A(nrows*ncols);

      // set to SPD
      {
        int seed = 13;
        srand(seed);
        std::vector<double> global_A(N*M);
        auto gind = [&] (const int row, const int col)
        {
          return col*M+row;
        };

        for (unsigned int c = 0; c < N; ++c)
          for (unsigned int r = c; r < M; ++r)
            {
              const double val = ((double) rand())/ ((double)RAND_MAX);
              if (r==c)
                global_A[gind(r,c)] = val + N;
              else
                {
                  global_A[gind(r,c)] = val;
                  global_A[gind(c,r)] = val;
                }
            }

        int k = 0;
        for (int c=1; c <= ncols; ++c)
          {
            const int glob_c = indxl2g_ (&c, &Nb, &mycol, &first_process_column, &proccols) - 1;
            for (int r = 1; r <= nrows; ++r)
              {
                const int glob_r = indxl2g_ (&r, &Mb, &myrow, &first_process_row, &procrows) - 1;
                A[k] = global_A[gind(glob_r,glob_c)];
                k++;
              }
          }
      }

      const int submatrix_row = 1;
      const int submatrix_column = 1;

      double *A_loc = &A[0];
      double *B_loc = &B[0];

      std::vector<double> ev(N);

      char jobz = 'N';
      char range = 'I';
      double vl = 0, vu = 0;
      int il = N-5, iu = N; // last 5 eigenvalues
      int nz = 0;
      double abstol = 0;
      double orfac = 0;
      std::vector<int> ifail;
      std::vector<int> iclustr;
      std::vector<double> gap(nrows*ncols);

      // get work sizes:
      int lwork = -1;
      int liwork = -1;
      double *eigenvectors_loc = nullptr; // won't need eigenvectors at all
      std::vector<double> work(1);
      std::vector<int> iwork(1);

      char cmach = 'S';
      abstol = pdlamch_(&ctxt, &cmach);
      abstol *= 2;
      ifail.resize(N);
      iclustr.resize(2*procrows*proccols);
      gap.resize(procrows*proccols);

      info = 0;
      if (mpiroot)
        cout << "pdsyevx_..."<< std::endl;

      pdsyevx_(&jobz,
                 &range,
                 &uplo,
                 &N,
                 A_loc,
                 &submatrix_row,
                 &submatrix_column,
                 descA,
                 &vl,
                 &vu,
                 &il,
                 &iu,
                 &abstol,
                 &N,
                 &nz,
                 &ev[0],
                 &orfac,
                 eigenvectors_loc,
                 &submatrix_row,
                 &submatrix_column,
                 descB,
                 &work[0],
                 &lwork,
                 &iwork[0],
                 &liwork,
                 &ifail[0],
                 &iclustr[0],
                 &gap[0],
                 &info);

      if (info != 0)
        {
          cerr << "error in pdpotrf_ "<< info << std::endl;
          return 1;
        }

      if (mpiroot)
        std::cout << "done!"<< std::endl;

      Cblacs_gridexit(ctxt);
    }
  MPI_Finalize();
}

Hello everyone, 

I have been trying to compile one of the example source codes using "icl". I have never had issues compiling Intel MKL code by linking to the MKL libraries: icl mkl_code.cpp  mkl_intel_lp64.lib mkl_tbb_thread.lib mkl_core.lib tbb.lib.

The DAAL example code builds perfectly from VS2017, but whenever I tried to compile the code using Intel Compiler in the command line:

 icl lin_reg_qr_dense_batch.cpp daal_core.lib daal_thread.lib tbb.lib

I get the following error: 

lin_reg_qr_dense_batch.cpp

lin_reg_qr_dense_batch.cpp(31): catastrophic error: cannot open source file "service.h"

  #include <service.h>

                                   ^

compilation aborted for lin_reg_qr_dense_batch.cpp (code 4)

Any help would be appreciated.  

Thank you,

Ali

I need your help.

My application is running on IA32.  I use mkl_malloc(), but, I don't know exactl what  i should enter number is 32 or 64 for IA32.

I am wating your advice.

I wrote a sample code to test mkl_malloc as below.

When I run this code, I encounted memory error. 

Could somebody explain to me why this happened and how to slove this problem??

typedef struct _a_
{
	int *data;
	_a_()
	{
		data = (int *)mkl_malloc(10 * sizeof(int), 32);
		for (int i = 0; i < 10; i++)
		{
			data[i] = i;
		}
	}
	
	~_a_()
	{
		mkl_free(data);
	}
}a_;

int main(void)
{
	a_ *b;
	b = (a_ *)mkl_malloc(10 * sizeof(a_), 32);

	std::cout << b[0].data[0] << std::endl;


	mkl_free(b);
	return 0;
}

In the C version of MKL's tutorial, source file dgemm_example.c, line 71:

    cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, 
                m, n, p, alpha, A, p, B, n, beta, C, n);

Where is the varialble `CblasRowMajor` declared and where is the value of this variable set? I couldn't find any clue about it in the source file. Thank you for your help.

I have integrated MKL into Visual Studio 2015. The MKL developer guide says that as long as I select the function name and press F1, the context-sensitive help will open the function description in Web browser. But when I follow this instruction and press F1 on a MKL function, it is "Welcome to Visual Studio 2015" that is opened instead of the help page for MKL function. What is the matter and how to fix it?

Hi,

We are currently developing a distributed version of our c++ finite element program. We planned to use the Intel Direct Sparse Solver for Cluster but it seems we can't reach good scalability with our settings. The matrix is assumed non symmetric and built in the DCSR format.

The test case used is a simple thermal diffusion problem on a square grid.  Different sizes of problem, ranging from 1M to 25M DOF, have been tested with many combinations of MPI processes and OpenMP threads (usually with 1 MPI process by node or by socket). Memory allocated at factorization phase is scaling down but we observed small speed-up on running time.

Actually, we observed these behaviors:

- Symbolic factorization benefits from more MPI processes but is not affected by threads.

- Factorization scales with number of OpenMP threads and sometimes with MPI.

- Most of the time, results shows no significant gain  on solving phase for both parallelization.

I must be doing something wrong but i can't  seem to find the solution to the problem.

Thanks a lot for any advice

The following iparm variables are used:

iparm(0) = 1;

iparm(1) = 10;

iparm(7) = 2;

iparm(9) = 13;

iparm(10) = 1;

iparm(12) = 1;

iparm(34) = 1;

iparm(39) = 2;

iparm(40,41) = first and last line of local matrix

The code is compiled with  2017 Intel compiler and Intel MPI. Compilation flags used are :  -03 -qopenmp -mkl=parallel and