atlas

The theano installation documentation states, that theano will as a default use the BLAS libraries from numpy, if the "BLAS libraries are available as dynamically-loadable libraries". This seems not to be working on my machine, see error message. How do I find out, if the numpy BLAS libraries are availalbe as dynamically-loadable? How do I recompile the numpy BLAS libraries, if they are not dynamically-loadable? Please indicate, if you would need more information! Error message We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure

I've been porting MATLAB code over to Python and, after quite a lot of work, I have stuff that works. The downside, however, is that Python is running my code more slowly than MATLAB did. I understand that using optimised ATLAS libraries will speed things up, but actually implementing this is confusing me. Here's what's going on: I start an ipython session with no BLAS installed: import numpy.distutils.system_info as sysinfo import time In [11]: sysinfo.get_info('atlas') Out[11]: {} timeit( eig(randn(1E2,1E2)) ) 100 loops, best of 3: 13.4 ms per loop The same code in Matlab runs twice as fast

I don't understand how BLAS, LAPACK and ATLAS are related and how I should use them together! I have been looking through all of their manuals and I have a general idea of BLAS and LAPACK and how to use them with the very few examples I find, but I can't find any actual examples using ATLAS to see how it is related with these two. I am trying to do some low level work on matrixes and my primary language is C. First I wanted to use GSL, but it says that if you want the best performance you should use BLAS and ATLAS. Is there any good webpage giving some nice examples of how to use these (in C)

I installed atlas, blas and lapack x86_64 packages via yum install atlas.x86_64 blas.x86_64 lapack.x86_64 on a Redhat 6.6 (ii) distro which installs a shared library but am having problems compiling and linking. For example, if I try to compile the minimal working example: program main print *, 'hello world' end program main using gfortran -L. main.f90 -llapack -lblas -o main the compiler doesn't find the libraries and I get the error message: /usr/bin/ld: cannot find -llapack collect2: ld returned 1 exit status I'm relatively new to fortran and linux so I'm probably missing something obvious.

今天 360 团队在 GitHub 上发布了 Atlas-2.0.0 版本。可喜可贺，希望 360 的开发团队能够一直保持下去。借此时机，将之前对 Atlas-1.0.3 的一些理解和使用，以博客形式总结一下。本文主要讲一下在源码安装和刚开始运行 Atlas-1.0.3 时可能遇到的问题。 ===== 按照安装说明，Atlas-1.0.3 的安装有如下依赖项： glib(2.32.x)、libevent(1.4以上)、Lua(5.1.x)、OpenSSL(0.9.8以上) 基本执行命令如下： ./bootstrap.sh #可能需要修改其中的路径 make sudo make install 依赖项的安装不做详细说明，假设已经全部安装好了。 ===== 1. 执行 bootstrap.sh 下面是源码包中提供的 bootstrap.sh 脚本的内容： [root@Betty Atlas-1.0.3]# vi bootstrap.sh #!/bin/sh base=$(cd "$(dirname "$0")"; pwd) cd $base PKG_CONFIG_PATH=/usr/local/lib/pkgconfig ./configure --with-mysql=/usr --prefix=/usr/local/mysql-proxy CFLAGS="-DHAVE_LUA_H

I singed up freely at mongodb Atlas and created cluster now I want to know how can I create database and connect to that using robomongo? kdblue 1) (Atlas Mongodb console)First of all click on ALLOW ACCESS FROM ANYWHERE (see in below image) and put some random IP address , don't click on Add Current IP Address otherwise it will not connect with robomongo . 2) Now open robomongo ,select connection Tab and then select type Direct Connection , and put your primary cluster in Address [you can get your Primary Cluster Address from Project->Clusters->(choose) Primary Cluster-> "There you will find

I found that using one of BLAS/ATLAS/MKL/OPENBLAS will give improvement on speed in R. However, will it still improve the R Package that is written in C or C++? for example, R package Glmnet is implemented in FORTRAN and R package rpart is implemented in C++. Will it just installing BLAS/...etc will improve the execution time? or do we have to rebuild (building new C code) the package based on BLAS/...etc? It is frequently stated, including in a comment here, that "you have to recompile R" to use different BLAS or LAPACK library. That is wrong. You do not have to recompile R provided it is