参考网站：

http://blog.csdn.net/10km/article/details/52723306

http://stackoverflow.com/questions/31395729/how-to-enable-multithreading-with-caffe

 

 

 

 

一、安装openblas

 

要利用openblas进行加速

安装

  1. sudo yum install openblas-devel

 

查看安装的路径

  1. rpm -ql openblas-devel

 

 1 /usr/include/openblas
 2 /usr/include/openblas/cblas.h
 3 /usr/include/openblas/f77blas.h
 4 /usr/include/openblas/lapacke.h
 5 /usr/include/openblas/lapacke_config.h
 6 /usr/include/openblas/lapacke_mangling.h
 7 /usr/include/openblas/lapacke_utils.h
 8 /usr/include/openblas/openblas_config.h
 9 /usr/lib64/libopenblas.so
10 /usr/lib64/libopenblaso.so
11 /usr/lib64/libopenblasp.so

 

 

 

/usr/include/openblas

/usr/include/openblas/cblas.h

/usr/include/openblas/f77blas.h

/usr/include/openblas/lapacke.h

/usr/include/openblas/lapacke_config.h

/usr/include/openblas/lapacke_mangling.h

/usr/include/openblas/lapacke_utils.h

/usr/include/openblas/openblas_config.h

/usr/lib64/libopenblas.so

/usr/lib64/libopenblaso.so

/usr/lib64/libopenblasp.so

二、配置Makefile.config

 

BLAS := open

# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.

# Leave commented to accept the defaults for your choice of BLAS

# (which should work)!

BLAS_INCLUDE := /usr/include/openblas

BLAS_LIB := /usr/lib64/openblas

  1. make clean
  2. make all

三、报错

make: /usr/local/bin/bin/mexext: Command not found

 

目测：matlab路径没有配置正确

查找matlab安装路径

  1. sudo find /usr/local -name mexext

/usr/local/MATLAB/R2014a/bin/mexext

配置Makefile.config中MATLAB路径

MATLAB_DIR := /usr/local/MATLAB/R2014a

 

  1. Make clean
  2. Make –j64 all

成功

查看caffe指向的依赖库

  1. ldd build/tools/caffe

linux-vdso.so.1 => (0x00007fffff5ff000)

    libcaffe.so.1.0.0-rc3 =>

    libopenblas.so.0 => /usr/lib64/libopenblas.so.0 (0x00007f17b58de000)

    ******

 

    libnppc.so.6.5 => /usr/local/cuda-6.5/lib64/libnppc.so.6.5 (0x00007f4d257c6000)

运行后照旧是单核。

在/usr/lib64下不仅有libopenblas.so.0(单线程版本)，还有一个libopenblasp.so.0,这个就是前面软件列表中的openblas-openmp的so文件(多线程版本)

redhat配置caffe多核训练如何利用Makefile配置openblas的库文件没有搞明白。

因此，查看教程，利用CMake来指定openbals的库为多线程版本，重新编译caffe。

http://blog.csdn.net/10km/article/details/52723306

四、CMake编译caffe

redhat配置caffe多核训练

于是修改OMP_NUM_THREADS或(OPENBLAS_NUM_THREADS)参数减少OpenBlas的线程数再试

  1. export OMP_NUM_THREADS=4
  2. export OPENBLAS_NUM_THREADS=4
  3. echo $ OMP_NUM_THREADS
  4. echo $ OPENBLAS_NUM_THREADS

关于OMP_NUM_THREADS和OPENBLAS_NUM_THREADS的详细用法说明参见《OpenBlas github网站》

重新运行mnist，可以看到cpu跑满4核。

redhat配置caffe多核训练

但是到4核以上会报错

redhat配置caffe多核训练

Segmentation fault，至今没有发现原因。