blas

问题 I would like to write a program that makes extensive use of BLAS and LAPACK linear algebra functionalities. Since performance is an issue I did some benchmarking and would like know, if the approach I took is legitimate. I have, so to speak, three contestants and want to test their performance with a simple matrix-matrix multiplication. The contestants are: Numpy, making use only of the functionality of dot . Python, calling the BLAS functionalities through a shared object. C++, calling the

问题 I am builing my numpy/scipy environment based on blas and lapack more or less based on this walk through. When I am done, how can I check, that my numpy/scipy functions really do use the previously built blas/lapack functionalities? 回答1: The method numpy.show_config() (or numpy.__config__.show() ) outputs information about linkage gathered at build time. My output looks like this. I think it means I am using the BLAS/LAPACK that ships with Mac OS. >>> import numpy as np >>> np.show_config()

问题 I'm trying to compile someone else's code and there isn't a simple header file. I need to link the default lapack and blas libraries on osx, and after some searching I figured they're in the Accelerate framework, and in my make.inc I used something like LAPACK_LIBS = -framework Accelerate Now I read in the man page that I should include #include <Accelerate/Accelerate.h> Is there a way I can do it in make.inc? Sorry if this is dumb I'm not really a programmer and I've tried my best to search.

问题 I have to calculate some products in the form A'A or more general A'DA , where A is a general mxn matrix and D is a diagonal mxm matrix. Both of them are full rank; i.e. rank(A)=min(m,n) . I know that you can save a substantial time is such symmetric products: given that A'A is symmetric, you only have to calculate the lower --or upper-- diagonal part of the product matrix. That adds to n(n+1)/2 entries to be calculated, which is roughly the half of the typical n^2 for large matrices. This is

问题 Benchmark test results on the home page of Julia (http://julialang.org/) shows that Fortran is about 4x slower than Julia/Numpy in the "rand_mat_mul" benchmark. I can not understand that why fortran is slower while calling from the same fortran library (BLAS)?? I have also performed a simple test for matrix multiplication evolving fortran, julia and numpy and got the similar results: Julia n = 1000; A = rand(n,n); B = rand(n,n); @time C = A*B; >> elapsed time: 0.069577896 seconds (7 MB

问题 I'm building numpy from source on CentOS 6.5 with no root access (python -V=2.7.6). I have the latest numpy source from git. I cannot for the life of me get numpy to acknowledge atlas libs. I have: ls -1 /usr/lib64/atlas libatlas.so.3 libatlas.so.3.0 libcblas.so.3 libcblas.so.3.0 libclapack.so.3 libclapack.so.3.0 libf77blas.so.3 libf77blas.so.3.0 liblapack.so.3 liblapack.so.3.0 libptcblas.so.3 libptcblas.so.3.0 libptf77blas.so.3 libptf77blas.so.3.0 I don't know anything about how these libs

问题 I need to sum all the elements of a matrix in caffe, But as I noticed, the caffe wrapper of the cblas functions ( 'math_functions.hpp' & 'math_functions.cpp' ) is using cblas_sasum function as caffe_cpu_asum that computes the sum of the absolute values of elements in a vector. Since I'm a newbie in cblas, I tried to find a suitable function to get rid of absolute there, but it seems that there is no function with that property in cblas. Any suggestion? 回答1: There is a way to do so using cblas

问题 My Dear All the Greatest Lords, It's most simple to install software packages in common use. But by means of such oversimplified routines lost control of varieties under different context of developing environments. After downloading, unzipping, plugging in the openblas2, I expect to enjoy the fortunate linear algebra computing functionality provided. Having succeeded in compiling a bunch of codes based on them, gcc broke off by encountering the following error, "cannot find -lgoto" Thanks in

问题 I am working on Windows 10 64-bit using MSYS2 with the 64-bit toolchain. In the MSYS2 terminal I found and installed the following packages after searching for them with pacman -Ss , mingw64/mingw-w64-x86_64-openblas 0.2.20-2 mingw64/mingw-w64-x86_64-lapack 3.6.1-1 mingw64/mingw-w64-x86_64-arpack 3.5.0-1 I took an example fortran source file from the Intel MKL library examples; I copied the dgelsx.f file to a folder in my msys home directory. I compile (in the mingw64 terminal) with gfortran

问题 I'm trying to link Ipopt with Intel MKL (instructions). Intel's Link Advisor suggests: Link line: -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_core.a ${MKLROOT}/lib/intel64/libmkl_intel_thread.a -Wl,--end-group -lpthread -lm -ldl Compiler options: -DMKL_ILP64 -qopenmp -I${MKLROOT}/include I try to configure Ipopt with: ../configure CXX=icpc CC=icc F77=ifort --with-blas=" -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib