multithreading,optimization,lapack,blas,eigenvalue

You are correct expecting multi-threaded behavior mainly from BLAS and not LAPACK routines. The size of the matrices is big enough to utilize multi-threaded environment. I am not sure about the extend of BLAS usage in ZGGEV routine, but it should be more than a spike. Regarding your specific questions....

You are looking for is BLAS_DIR and LAPACK_DIR variable. set(BLAS_DIR /path/to/blas) find_package(BLAS REQUIRED) set(LAPACK_DIR /path/to/lapack) find_package(LAPACK REQUIRED) ...

python,numpy,floating-point,blas

Floating point calculations are not always reproducible. You may get reproducible results for floating calculations across different machines if you use the same executable image, inputs, libraries built with the same compiler and identical compiler settings (switches). However if you use a dynamically linked library you may get different results,...

I posted the question on the R-SIG-Fedora mailing list and this morning I've got the following answer from Martyn Plummer (part of R core team and responsible for the R-SIG-Fedora mailing list at [email protected]): This is a change in the way that the RPM is built. The RPM for R...

numpy,ubuntu-14.04,blas,openblas

Based on the ldd output, NumPy must already be linked with OpenBLAS. It just doesn't know it, because it's linked via /usr/lib/libblas*, which it sees as generic BLAS.

I assume, that if you are new to C++, you are also new to C and Fortran. In that case I would definitely suggest to you, not to start with Blas/Lapack, at least not without a nice C++-wrapper. My suggestion would be to have a look at Eigen which offers...

matlab,numpy,matrix,matrix-multiplication,blas

Introduction and Solution Code np.einsum, is really hard to beat, but in rare cases, you can still beat it, if you can bring in matrix-multiplication into the computations. After few trials, it seems you can bring in matrix-multiplication with np.dot to surpass the performance with np.einsum('ia,aj,ka->ijk', A, B, C). The...

You are right, the problem is in incx value - it should be 1, take a look at reference. INCX is INTEGER On entry, INCX specifies the increment for the elements of X. INCX must not be zero. So this value should be used when values of vector x is...

When you give the LDx you declare the actual size (leading dimension) of the matrix. Then BLAS uses this value to skip the not used data from the multiplication. I think you should just use dgemm same way as in C. I mean you should not pass the submatrix a(1:100,101:200)...

optimization,docker,lapack,blas

No, I think you're pretty much right. The image you link to is an automated build, so OpenBlas will be getting compiled using the kernel from the Docker build server. I did notice the build script sets the following flags when building OpenBlas: DYNAMIC_ARCH=1 NO_AFFINITY=1 NUM_THREADS=32 Which presumably makes the...

To the best of my knowledge, BLAS/LAPACK gemm has never supported in-place operations, ie. C := alpha*op( A )*op( B ) + beta*C cannot be transformed into A := alpha*op( A )*op( B ) + beta*A or B := alpha*op( A )*op( B ) + beta*B with any guarantee of...

A while agon, I read in a book: (1) Golden rule about optimization: don't do it (2) Golden rule about optimization (for experts only): don't do it yet. In short, I'd recommend to proceed as follows: step 1: implement the algorithms in the simplest / most legible way step 2:...

In include dir there is openblas_config.h You can find openblas version there. i.e. grep OPENBLAS_VERSION /usr/local/include/openblas_config.h ...

The BLAS routine is used correctly. The only difference is that BLAS is performing C = 0.0*C + 1.0*A*B and your loop C = C + A*B In your loop you are trying to improve usage of cpu's cache memory. There are variants of BLAS that perform similar actions. I...

parallel-processing,fortran,lapack,blas,scalapack

Scalapack library uses naming conversion to declare single or double precision function. This declaration is done by the second letter of scalapack function The "PD*" function means a double precision function, while "PS*" means single. So, you should change DBLESZ = 8 to DBLESZ = 4 All DOUBLE PRECISION to...

The issue is on the lda. From the reference we get that lda: The size of the first dimension of matrix A The CblasRowMajor and CblasColMajor describe the memory storage sequence of a two dimensional matrix. The CblasRowMajor storage of a matrix A(nrow,ncol) means that first are stored the ncol...

You need to install the *-devel versions of those packages. E.g., with a virtual Fedora 17 system I had lying around: $ cat main.f90 program main print *, 'hello world' end program main $ gfortran -L. main.f90 -llapack -lblas -o main /usr/bin/ld: cannot find -llapack /usr/bin/ld: cannot find -lblas collect2:...

c++,lapack,blas,absolute-value,argmax

BLAS was designed to provide low-level routines necessary to implement common linear-algebra operations (it is the "Basic Linear Algebra Subprograms", after all). To name just one of many uses, getting the largest-magnitude element of a vector is necessary for pivot selection in LU factorization, which is one of the most...

Key observation is that Armadillo exp() function is way slower than MATLAB. Similar overhead is observed in log(), pow() and sqrt().

There are two issues, one of them is undefined behavior, the other one probably just a minor oversight in reading the documentation: for (i = 0;i < 10;i++){ printf ("%d ",x[i]); } You use the format specifier %d for a double value. Use %f instead. for (i = 0;i <...

I had the same problem as you. Under OSX overriding the library instead of replacing the library in the R.framework solved the problem for me: $ DYLD_FORCE_FLAT_NAMESPACE=y DYLD_INSERT_LIBRARIES=/Developer/NVIDIA/CUDA-7.0/lib/libnvblas.7.0.dylib R ...

c++,multithreading,gcc,c++11,blas

The answer is fairly simple: Your threads are fighting for memory bandwidth! Consider that you perform one floating point addition per 2 stores (one initialization, one after the addition) and 2 reads (in the addition). Most modern systems providing multiple cpus actually have to share the memory controller among several...

parallel-processing,julia-lang,blas

You're running into several issues, of which the most important is that Xt[:,i] creates a new array (allocating memory). Here's a demonstration that gets you closer to what you want: n = 10000; p = 25000 # make normal Arrays x = randn(n,p) y = ones(p) z = zeros(n) #...

According to the documentation of sgtsv(), the upper (DU) and lower (DL) diagonals of the tridiagonal matrix A will be overwritten as the linear equation is solved. On exit, DL is overwritten by the (n-2) elements of the second super-diagonal of the upper triangular matrix U from the LU factorization...

c++,distributed-computing,blas,intel-mkl,scalapack

The issue may come from : MPI_Bcast(&lda, 1, MPI_INT, 0, MPI_COMM_WORLD); Before this line, lda is different on each process if the dimension of the matrix is odd. Two processes handle 2 rows and two processes handle 3 rows. But after the MPI_Bcast(), lda is the same everywhere (3). The...

c,matrix,vector,blas,intel-mkl

Short answer is, yes you can use dgemm for rank-1 update. The dger is of course suggested, since it is expected to be better optimized for this operation. As far as the use of cblas_dgemm . As you know the definition of leading dimension is: lda: The size of the...

What you need is an iterative eigenvalue solve algorithm. LAPACK uses a direct eigensolver and having an estimation of eigenvectors is of no use. There is a QR iterative refinement in its routines. However It requires Hessenberg matrices. I do not think you could use these routines. You could use...

python,numpy,install,lapack,blas

Alright, here is the whole story. First, the initial setup was slow because BLAS is a reference implementation which is not designed to be fast. I repeat, as of today, the package blas in the ArchLinux Extra repository is the reference implementation. For details, see the Presentation section here. Secondly,...

multithreading,fortran,lapack,blas

The LAPACK library is expected to be thread safe. It does not support multiple threads, so it does not use (all) your systems cores. Actually there is a specific declaration that all LAPACK subroutines are thread-safe since v3.3. On the other hand LAPACK is designed to use extensively BLAS library...