blas

Why is numpy's kron so fast?

由 旧城冷巷雨未停 提交于 2020-06-08 14:03:06
问题 I was trying to implement a kronecker product function. Below are three ideas that I have: def kron(arr1, arr2): """columnwise outer product, avoiding relocate elements. """ r1, c1 = arr1.shape r2, c2 = arr2.shape nrows, ncols = r1 * r2, c1 * c2 res = np.empty((nrows, ncols)) for idx1 in range(c1): for idx2 in range(c2): new_c = idx1 * c2 + idx2 temp = np.zeros((r2, r1)) temp_kron = scipy.linalg.blas.dger( alpha=1.0, x=arr2[:, idx2], y=arr1[:, idx1], incx=1, incy=1, a=temp) res[:, new_c] = np

Error in linking gfortran to LAPACK and BLAS

由 a 夏天 提交于 2020-05-26 06:29:25
问题 I have installed LAPACK and BLAS from Synaptic package manager in Ubuntu. whereis libblas libblas: /usr/lib/libblas.so /usr/lib/libblas.a /usr/lib/libblas whereis liblapack liblapack: /usr/lib/liblapack.a /usr/lib/liblapack.so When I try to compile the randomsys1 example with gfortran I get the following error messages. gfortran randomsys1.f90 -L/usr/lib/lapack -llapack -L/usr/lib/libblas -lblas /tmp/cclwtifh.o: In function `MAIN__': randomsys1.f90:(.text+0x12): undefined reference to `init

CMake is not able to find LAPACK sgemm on macOS

由 生来就可爱ヽ(ⅴ＜●) 提交于 2020-05-16 22:36:27
问题 I'm trying to compile this code, but when running cmake .. in the build folder I get the messages: -- Looking for Fortran sgemm -- Looking for Fortran sgemm - not found -- Looking for pthread.h -- Looking for pthread.h - found -- Performing Test CMAKE_HAVE_LIBC_PTHREAD -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success -- Found Threads: TRUE -- Looking for Fortran dgemm -- Looking for Fortran dgemm - found -- Found BLAS: /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System

In R how to control multi-threading in BLAS parallel matrix product

由 瘦欲@ 提交于 2020-05-13 07:27:46
问题 I have a question regarding the use of BLAS parallelized matrix product in R (being the default matrix product at least since R-3.4, maybe earlier). The default behavior (at least on my machine) is now for the matrix product (c.f. example below) to use all the cores available on the machine, which can be a problem. Do you know how to control the number of cores used for standard matrix product in R? Thanks in advance Example: n=10000 p=1000 q=5000 A = matrix(runif(n*p),nrow=n, ncol=p) B =

最全caffe安装踩坑记录（Anaconda,nvidia-docker,Linux编译）

由 时光毁灭记忆、已成空白 提交于 2020-05-04 03:42:11
Anaconda,nvidia-docker,Linux三种方式安装caffe 1.Anaconda安装caffe 　　1.首先安装 anaconda 　　2.创建虚拟环境（python2.7）　　 conda create -n caffe python= 2.7 anaconda 　　3.安装caffe # 然后下面二选一即可，安装caffe conda install -c conda- forge caffe conda install -c conda-forge/label/broken caffe 　　4.注意： # 如果出现numpy导入错误，是因为 conda- forge中的numpy版本过低，可再输入下面命令 pip install numpy --upgrade 　　5.测试 # 测试是否成功 source activate caffe python import caffe 2.nvidia-docker安装caffe-gpu 注意：docker仅支持cpu nvidia -docker支持GPU 　　1.安装nvidia-docker wget -P /tmp https: // github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb

pyinstaller发布exe，弹出Failed to execute script main

由 十年热恋 提交于 2020-04-26 08:17:08
1.在PyCharm中按Alt+F12打开Terminal对话框 1.1我的项目文件放在wxpython目录下，D:\learn\Weather index insurance\wxpython>pyinstaller -F -w teaindex_main.py 1.2运行结束后在D:\learn\Weather index insurance\wxpython目录下生成dist文件夹和build文件夹，我的exe程序在dist文件夹下，双击exe弹出： 1.3重新在Terminal对话框中，运行D:\learn\Weather index insurance\wxpython>pyinstaller -F -c teaindex_main.py ，运行exe，可以看到问题所在，缺少module typedefs 应该使用：pyinstaller -F -c demo.py 此时打包完成后，点击exe执行文件，如果有报错的话，将在控制台显示。这是，要做好截图的准备，因为控制台报错后是一闪而过的。 1.4查阅网上的解决方法 将打包代码改成： D:\learn\Weather index insurance\wxpython>pyinstaller -F -c teaindex_main.py --hidden-import sklearn.neighbors.typedefs

How do I use the BLAS library provided by MATLAB?

由 帅比萌擦擦* 提交于 2020-02-04 05:31:05
问题 I have noticed that MATLAB provides the BLAS and LAPACK headers among others: $ ls ${MATLAB_DIR}/extern/include/ blas.h engine.h lapack.h mat.h mclmcr.h mex.h mwutil.h blascompat32.h fintrf.h libmatlbm.mlib matrix.h mclmcrrt.h mwdebug.h tmwtypes.h emlrt.h io64.h libmatlbmx.mlib mclcppclass.h mcr.h mwservices.h I would like to use them for my compiled code, so I tried to compile the following: ex_blas.c #include <blas.h> int main() { return 0; } with the following command: $ mex ex_blas.c But

'Symbol lookup error' with netlib-java

由 ∥☆過路亽．° 提交于 2020-01-22 20:56:26
问题 Background & Problem I am having a bit of trouble running the examples in Spark's MLLib on a machine running Fedora 23. I have built Spark 1.6.2 with the following options per Spark documentation: build/mvn -Pnetlib-lgpl -Pyarn -Phadoop-2.4 \ -Dhadoop.version=2.4.0 -DskipTests clean package and upon running the binary classification example: bin/spark-submit --class org.apache.spark.examples.mllib.BinaryClassification \ examples/target/scala-*/spark-examples-*.jar \ --algorithm LR --regType

numpy.disutils.system_info.NotFoundError: no lapack/blas resources found

由 点点圈 提交于 2020-01-17 05:37:07
问题 Problem: Linking numpy to correct Linear Algebra libraries. Process is so complicated that I might be looking for the solution 6th time and I have no idea whats going wrong. I am on Ubuntu 12.04.5. I reinstalled blas and lapack and then reinstalled numpy using pip. I did that in system environment and then also tried in virtualenv environment. None of the things seem to be working. Here is my numpy.__config__.show() : lapack_info: NOT AVAILABLE lapack_opt_info: NOT AVAILABLE openblas_lapack

Do BLAS and LAPACK libraries comply with MISRA standard?

由 血红的双手。 提交于 2020-01-16 08:35:55
问题 I guess the answer is no . However, I was wondering if someone has some insight into this topic. Do BLAS and LAPACK libraries comply with MISRA standards? The MISRA standards (MISRA C:1998, MISRA C:2004, MISRA C:2012) are extremly demanding, and I believe that BLAS and LAPACK libraries do not comply with it. Hence, I should not use such libraries if my software project demands MISRA compliance. Any insight into this question would be extremly appreciated :) 回答1: Unless the library is actively
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