blas

Armadillo + BLAS + LAPACK: Linking error?

由 萝らか妹 提交于 2019-11-28 01:15:01
问题 When I try to compile example1.cpp that comes with Armadillo 2.4.2, I keep getting the following linking error: /tmp/ccbnLbA0.o: In function `double arma::blas::dot<double>(unsigned int, double const*, double const*)': main.cpp:(.text._ZN4arma4blas3dotIdEET_jPKS2_S4_[double arma::blas::dot<double>(unsigned int, double const*, double const*)]+0x3b): undefined reference to `wrapper_ddot_' /tmp/ccbnLbA0.o: In function `void arma::blas::gemv<double>(char const*, int const*, int const*, double

TensorFlow: InternalError: Blas SGEMM launch failed

由 那年仲夏 提交于 2019-11-27 17:47:34
When I run sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) I get InternalError: Blas SGEMM launch failed . Here is the full error and stack trace: InternalErrorTraceback (most recent call last) <ipython-input-9-a3261a02bdce> in <module>() 1 batch_xs, batch_ys = mnist.train.next_batch(100) ----> 2 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) /usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata) 338 try: 339 result = self._run(None, fetches, feed_dict, options_ptr, --> 340 run_metadata_ptr)

Calling MATLAB's built-in LAPACK/BLAS routines

由 拜拜、爱过 提交于 2019-11-27 14:08:54
I want to learn how to call the built-in LAPACK/BLAS routines in MATLAB. I have experience in MATLAB and mex files but I've actually no idea how to call LAPACK or BLAS libraries. I've found the gateway routines in file exchange that simplifies the calls since I don't have to write a mex file for any function such as this one . I need any toy example to learn the basic messaging between MATLAB and these built-in libraries. Any toy example such as matrix multiplication or LU decomposition is welcome. If you look inside the lapack.m file from the FEX submission mentioned, you will see a couple of

Linking Intel's Math Kernel Library (MKL) to R on Windows

由 随声附和 提交于 2019-11-27 13:43:54
问题 Using an alternative BLAS for R has several advantages, see e.g. https://cran.r-project.org/web/packages/gcbd/vignettes/gcbd.pdf. Microsoft R Open https://mran.revolutionanalytics.com/documents/rro/installation/#sysreq is using Intel's MKL instead of the default Reference BLAS to speed up calculations. My question is: What would be the exact steps to link Intel's MKL library **manually to R**'s most recent version on Windows (https://cran.r-project.org/bin/windows/base/)? UPDATE 20-07-2016:

Running Scipy on Heroku

由 左心房为你撑大大i 提交于 2019-11-27 11:35:07
问题 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 =>

MatLab error: cannot open with static TLS

由 大城市里の小女人 提交于 2019-11-27 10:23:21
Since a couple of days, I constantly receive the same error while using MATLAB which happens at some point with dlopen . I am pretty new to MATLAB, and that is why I don't know what to do. Google doesn't seem to be helping me either. When I try to make an eigenvector, I get this: Error using eig LAPACK loading error: dlopen: cannot load any more object with static TLS I also get this while making a multiplication: Error using * BLAS loading error: dlopen: cannot load any more object with static TLS I did of course look for the solutions to this problem, but I don't understand too much and don

Undefined reference to LAPACK and BLAS subroutines

由 青春壹個敷衍的年華 提交于 2019-11-27 08:26:29
问题 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'

Without root access, run R with tuned BLAS when it is linked with reference BLAS

由 狂风中的少年 提交于 2019-11-27 07:59:11
问题 Can any one tell me why I can not successfully test OpenBLAS's dgemm performance (in GFLOPs) in R via the following way? link R with the "reference BLAS" libblas.so compile my C program mmperf.c with OpenBLAS library libopenblas.so load the resulting shared library mmperf.so into R, call the R wrapper function mmperf and report dgemm performance in GFLOPs. Point 1 looks strange, but I have no choice because I have no root access on machines I want to test, so actual linking to OpenBLAS is

Calling BLAS / LAPACK directly using the SciPy interface and Cython

由 感情迁移 提交于 2019-11-26 23:33:56
问题 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

Benchmarking (python vs. c++ using BLAS) and (numpy)

由 天涯浪子 提交于 2019-11-26 23:30:32
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 BLAS functionalities through a shared object. Scenario I implemented a matrix-matrix multiplication for
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