blas

问题 I'd like to know if it's possible to change at (Python) runtime the maximum number of threads used by OpenBLAS behind numpy? I know it's possible to set it before running the interpreter through the environment variable OMP_NUM_THREADS , but I'd like to change it at runtime. Typically, when using MKL instead of OpenBLAS, it is possible: import mkl mkl.set_num_threads(n) 回答1: You can do this by calling the openblas_set_num_threads function using ctypes . I often find myself wanting to do this,

问题 Is there a means to do element-wise vector-vector multiplication with BLAS, GSL or any other high performance library ? 回答1: (Taking the title of the question literally...) Yes it can be done with BLAS alone (though it is probably not the most efficient way.) The trick is to treat one of the input vectors as a diagonal matrix: ⎡a ⎤ ⎡x⎤ ⎡ax⎤ ⎢ b ⎥ ⎢y⎥ = ⎢by⎥ ⎣ c⎦ ⎣z⎦ ⎣cz⎦ You can then use one of the matrix-vector multiply functions that can take a diagonal matrix as input without padding, e.g.

问题 Is there a means to do element-wise vector-vector multiplication with BLAS, GSL or any other high performance library ? 回答1: (Taking the title of the question literally...) Yes it can be done with BLAS alone (though it is probably not the most efficient way.) The trick is to treat one of the input vectors as a diagonal matrix: ⎡a ⎤ ⎡x⎤ ⎡ax⎤ ⎢ b ⎥ ⎢y⎥ = ⎢by⎥ ⎣ c⎦ ⎣z⎦ ⎣cz⎦ You can then use one of the matrix-vector multiply functions that can take a diagonal matrix as input without padding, e.g.

问题 When I'm trying to use TensorFlow with Keras using the gpu, I'm getting this error message: C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\__main__.py:2: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., 37800, epochs=2, validation_data=<keras.pre..., validation_steps=4200)` from ipykernel import kernelapp as app Epoch 1/2 InternalError Traceback (most recent call last) C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site

问题 I'm currently using BLAS function DSYMM to compute Y = AX and then DGEMM for YA' , but I'm wondering is there some more efficient way of computing the matrix product AXA T , where A is an arbitrary n×n matrix and X is a symmetric n×n matrix? 来源： https://stackoverflow.com/questions/11139933/efficient-way-of-computing-matrix-product-axa

问题 I'm attempting to do a release of some software and am currently working through a script for the build process. I'm stuck on something I never thought I would be, statically linking LAPACK on x86_64 linux. During configuration AC_SEARCH_LIB([main],[lapack]) works, but compilation of the lapack units do not work, for example undefiend reference to 'dsyev_' --no lapack/blas routine goes unnoticed. I've confirmed I have the libraries installed and even compiled them myself with the appropriate

问题 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

问题 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

问题 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

问题 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