blas

I have looked for an easy way to install/compile Numpy with OpenBLAS but didn't find an easy answer. All the documentation I have seen takes too much knowledge as granted for someone like me who is not used to compile software. There are two packages in Ubuntu related to OpenBLAS : libopenblas-base and libopenblas-dev . Once they are installed, what should I do to install Numpy again with them? Thanks! Note that when these OpenBLAS packages are installed, Numpy doesn't work anymore: it can't be imported: ImportError: /usr/lib/liblapack.so.3gf: undefined symbol: ATL_chemv. The problem occurs as

I got Numpy and Matplotlib running on Heroku, and I'm trying to install Scipy as well. However, Scipy requires BLAS[1] to install, which is not presented on the Heroku platform. After contacting Heroku support, they suggested me to build BLAS as a static library to deploy, and setup the necessary environment variables. So, I compiled libblas.a on a 64bit Linux box, and set the following variables as described in [2] : $ heroku config BLAS => .heroku/vendor/lib/libfblas.a LD_LIBRARY_PATH => .heroku/vendor/lib LIBRARY_PATH => .heroku/vendor/lib PATH => bin:/usr/local/bin:/usr/bin:/bin

I'm trying to understand how BLAS and LAPACK in Fortran work and so on, so I made a code that generates a matrix and inverts it. Here's the code program test Implicit none external ZGETRF external ZGETRI integer ::M complex*16,allocatable,dimension(:,:)::A complex*16,allocatable,dimension(:)::WORK integer,allocatable,dimension(:)::IPIV integer i,j,info,error Print*, 'Enter size of the matrix' Read*, M Print*, 'Enter file of the matrix' READ(*,*), A OPEN(UNIT=10,FILE = '(/A/)' ,STATUS='OLD',ACTION='READ') allocate(A(M,M),WORK(M),IPIV(M),stat=error) if (error.ne.0)then print *,"error:not enough

In cuBLAS, cublasIsamin() gives the argmin for a single-precision array. Here's the full function declaration: cublasStatus_t cublasIsamin(cublasHandle_t handle, int n, const float *x, int incx, int *result) The cuBLAS programmer guide provides this information about the cublasIsamin() parameters: If I use host (CPU) memory for result , then cublasIsamin works properly. Here's an example: void argmin_experiment_hostOutput(){ float h_A[4] = {1, 2, 3, 4}; int N = 4; float* d_A = 0; CHECK_CUDART(cudaMalloc((void**)&d_A, N * sizeof(d_A[0]))); CHECK_CUBLAS(cublasSetVector(N, sizeof(h_A[0]), h_A, 1,

I'm running an algorithm that is implemented in Python and uses NumPy. The most computationally expensive part of the algorithm involves solving a set of linear systems (i.e. a call to numpy.linalg.solve() . I came up with this small benchmark: import numpy as np import time # Create two large random matrices a = np.random.randn(5000, 5000) b = np.random.randn(5000, 5000) t1 = time.time() # That's the expensive call: np.linalg.solve(a, b) print time.time() - t1 I've been running this on: My laptop, a late 2013 MacBook Pro 15" with 4 cores at 2GHz ( sysctl -n machdep.cpu.brand_string gives me

There was a post on this here: https://gist.github.com/JonathanRaiman/f2ce5331750da7b2d4e9 which shows a great speed improvement by just calling the Fortran libraries (BLAS / LAPACK / Intel MKL / OpenBLAS / whatever you installed with NumPy). After many hours of working on this (because of deprecated SciPy libraries) I finally got it to compile with no results. It was 2x faster than NumPy. Unfortunately as another user pointed out, the Fortran routine is always adding the output matrix to the new results calculated, so it only matches NumPy on the 1st run. I.e. A := alpha*x*y.T + A . So that