I am trying to save an armadillo matrix (mat) to a hdf5 file. I am on a CentOS cluster and using anaconda without root privileges.

我正在尝试将一只犰螂矩阵(MAT)保存到一个hdf5文件中。我在CentOS集群上，在没有超级用户权限的情况下使用Anaconda。

Packages installed

I have created an environment arma12 in which I first installed hdf5 and then armadillo. conda env export --from-history result is

我已经创建了一个环境arma12，我首先在其中安装了hdf5，然后安装了Aradillo。Conda环境导出--从历史中导出的结果为

name: arma12
channels:
  - defaults
dependencies:
  - hdf5
  - armadillo
prefix: /home/keshav/.conda/envs/arma12

and the versions are hdf5-1.14.2 and armadillo-12.6.4. The detailed output of conda list is

版本为HDF5-1.14.2和Aradillo-12.6.4。Conda List的详细输出为

# packages in environment at /home/keshav/.conda/envs/arma12:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       2_gnu    conda-forge
armadillo                 12.6.4               h0a193a4_0    conda-forge
arpack                    3.7.0                hdefa2d7_2    conda-forge
c-ares                    1.19.1               hd590300_0    conda-forge
ca-certificates           2023.7.22            hbcca054_0    conda-forge
hdf5                      1.14.2          nompi_h4f84152_100    conda-forge
keyutils                  1.6.1                h166bdaf_0    conda-forge
krb5                      1.21.2               h659d440_0    conda-forge
libaec                    1.0.6                hcb278e6_1    conda-forge
libblas                   3.9.0           18_linux64_openblas    conda-forge
libcblas                  3.9.0           18_linux64_openblas    conda-forge
libcurl                   8.2.1                hca28451_0    conda-forge
libedit                   3.1.20191231         he28a2e2_2    conda-forge
libev                     4.33                 h516909a_1    conda-forge
libgcc-ng                 13.1.0               he5830b7_0    conda-forge
libgfortran-ng            13.1.0               h69a702a_0    conda-forge
libgfortran5              13.1.0               h15d22d2_0    conda-forge
libgomp                   13.1.0               he5830b7_0    conda-forge
liblapack                 3.9.0           18_linux64_openblas    conda-forge
libnghttp2                1.52.0               h61bc06f_0    conda-forge
libopenblas               0.3.24          pthreads_h413a1c8_0    conda-forge
libssh2                   1.11.0               h0841786_0    conda-forge
libstdcxx-ng              13.1.0               hfd8a6a1_0    conda-forge
libzlib                   1.2.13               hd590300_5    conda-forge
ncurses                   6.4                  hcb278e6_0    conda-forge
openssl                   3.1.2                hd590300_0    conda-forge
superlu                   5.2.2                h00795ac_0    conda-forge
zstd                      1.5.5                hfc55251_0    conda-forge

Code

The minimal test code which is used is

使用的最小测试代码是

#define ARMA_USE_HDF5
# include <iostream>
# include <armadillo>

using namespace std;
using namespace arma;

int main()
{       cout<< "Hello world"<< endl;
        arma_version ver;
        cout << "ARMA version: "<< ver.as_string() << endl;
        
        mat A(2,2, fill::ones);
        cout<< A<< endl;
        A.save(hdf5_name("A.hdf5", "A"));
        return 1;
}

and is compiled via gcc/12.2.0 with

并通过GCC/12.2.0编译，具有

g++ -std=c++17 -O3 test.cpp -I$(ARMA)/include -L$(ARMA)/lib -Wl,-rpath=$(ARMA)/lib -larmadillo -lhdf5

where ARMA=/home/keshav/.conda/envs/arma12. This compiles without errors and gives an executable a.out , which on ldd ./a.out gives result

其中arma=/home/keshav/.conda/envs/arma12。这将在没有错误的情况下进行编译，并生成一个可执行文件a.out，它在LDD上提供结果。

    linux-vdso.so.1 =>  (0x00007ffc5dffb000)
    libarmadillo.so.12 => /home/keshav/.conda/envs/arma12/lib/libarmadillo.so.12 (0x00002b8a88204000)
    libhdf5.so.310 => /home/keshav/.conda/envs/arma12/lib/libhdf5.so.310 (0x00002b8a88280000)
    libstdc++.so.6 => /home/keshav/.conda/envs/arma12/lib/libstdc++.so.6 (0x00002b8a886ac000)
    libm.so.6 => /lib64/libm.so.6 (0x00002b8a888a7000)
    libgcc_s.so.1 => /home/keshav/.conda/envs/arma12/lib/libgcc_s.so.1 (0x00002b8a88ba9000)
    libc.so.6 => /lib64/libc.so.6 (0x00002b8a88bc4000)
    libblas.so.3 => /home/keshav/.conda/envs/arma12/lib/./libblas.so.3 (0x00002b8a88f88000)
    libarpack.so.2 => /home/keshav/.conda/envs/arma12/lib/./libarpack.so.2 (0x00002b8a8b14e000)
    /lib64/ld-linux-x86-64.so.2 (0x0000563928b22000)
    libcrypto.so.3 => /home/keshav/.conda/envs/arma12/lib/./libcrypto.so.3 (0x00002b8a8b19d000)
    libcurl.so.4 => /home/keshav/.conda/envs/arma12/lib/./libcurl.so.4 (0x00002b8a8b6aa000)
    libpthread.so.0 => /lib64/libpthread.so.0 (0x00002b8a8b75b000)
    libsz.so.2 => /home/keshav/.conda/envs/arma12/lib/./libsz.so.2 (0x00002b8a8b977000)
    libz.so.1 => /home/keshav/.conda/envs/arma12/lib/./libz.so.1 (0x00002b8a8b982000)
    libdl.so.2 => /lib64/libdl.so.2 (0x00002b8a8b99d000)
    librt.so.1 => /lib64/librt.so.1 (0x00002b8a8bba1000)
    libgfortran.so.5 => /home/keshav/.conda/envs/arma12/lib/././libgfortran.so.5 (0x00002b8a8bdaa000)
    libnghttp2.so.14 => /home/keshav/.conda/envs/arma12/lib/././libnghttp2.so.14 (0x00002b8a8bf55000)
    libssh2.so.1 => /home/keshav/.conda/envs/arma12/lib/././libssh2.so.1 (0x00002b8a8bf83000)
    libssl.so.3 => /home/keshav/.conda/envs/arma12/lib/././libssl.so.3 (0x00002b8a8bfc8000)
    libgssapi_krb5.so.2 => /home/keshav/.conda/envs/arma12/lib/././libgssapi_krb5.so.2 (0x00002b8a8c06a000)
    libzstd.so.1 => /home/keshav/.conda/envs/arma12/lib/././libzstd.so.1 (0x00002b8a8c0be000)
    libquadmath.so.0 => /home/keshav/.conda/envs/arma12/lib/./././libquadmath.so.0 (0x00002b8a8c1d2000)
    libkrb5.so.3 => /home/keshav/.conda/envs/arma12/lib/./././libkrb5.so.3 (0x00002b8a8c20c000)
    libk5crypto.so.3 => /home/keshav/.conda/envs/arma12/lib/./././libk5crypto.so.3 (0x00002b8a8c2e2000)
    libcom_err.so.3 => /home/keshav/.conda/envs/arma12/lib/./././libcom_err.so.3 (0x00002b8a8c2fa000)
    libkrb5support.so.0 => /home/keshav/.conda/envs/arma12/lib/./././libkrb5support.so.0 (0x00002b8a8c301000)
    libkeyutils.so.1 => /home/keshav/.conda/envs/arma12/lib/./././libkeyutils.so.1 (0x00002b8a8c30f000)
    libresolv.so.2 => /lib64/libresolv.so.2 (0x00002b8a8c316000)

i.e. all libraries are linked.

即所有的库都是链接的。

Problem

But when I execute the file by ./a.out , the output is

但当我通过./a.out执行该文件时，输出为

Hello world
ARMA version: 12.6.4 (Cortisol Retox)
   1.0000   1.0000
   1.0000   1.0000

i.e. it prints the A matrix correctly, but the code is halted indefinitely (atleast for 5 mins). On pressing Ctrl-C to terminate, ls shows that no hdf5 file is created, instead a A.hdf5.tmp_* file is created after each run.

即，它正确地打印A矩阵，但代码无限期地停止(至少5分钟)。按Ctrl-C终止时，ls显示没有创建hdf5文件，而是在每次运行后创建一个A.hdf5.tmp_*文件。

Note: my CentOS supports anaconda version 2021.11 with conda version 4.10.3, which I am using. Any higher version of anaconda can't be installed because of GLIBC version.

注意：我的CentOS支持Anaconda版本2021.11和Conda版本4.10.3，我正在使用它。由于GLIBC版本的原因，无法安装任何更高版本的蟒蛇。

Steps taken

I was expecting a simple hdf5 file. This code runs if the A.save(hdf5_name("A.hdf5", "A")); is commented, (or replaced by saving to dat file, with no halting) but no hdf5 files either.

我期待的是一个简单的hdf5文件。如果A.save(hdf5_name(“A.hdf5”，“A”))；被注释(或替换为保存到dat文件，不会停止)，但也没有hdf5文件，则运行此代码。

I tried a bunch of things

我试了很多方法

1. run the code on a local machine with arma-12.6.4. Here, I am not using anaconda and armadillo is installed via cmake and hdf5 via homebrew. I ran this on Macbook Air M1. arma-12.6.4 can't find hdf5 by default. The hdf5 is in /opt/homebrew/Cellar/hdf5/1.14.2, and

g++ -std=c++17 test.cpp -DARMA_USE_HDF5 -larmadillo -lhdf5 -lm -Wl,-rpath,/opt/homebrew/Cellar/hdf5/1.14.2/lib/ -I/opt/homebrew/Cellar/hdf5/1.14.2/include -L/opt/homebrew/Cellar/hdf5/1.14.2/lib

executes. But ./a.out gives zsh: segmentation fault ./a.out.

执行死刑。但是./a.out会给出zsh：分段错误./a.out。

2. run the code on a local machine with arma-11.4.4, which is installed via cmake after deleting all instances of arma-12.6.4. Here there was no problem. g++ -O2 -std=c++17 -larmadillo test.cpp gave an executable file , which gave file A.hdf5 with termination instantly.


3. Installed arma-11.4.4 in new environment named arma11, but the same problem remained there. the executable file is linked similar as with arma12 but with arma11 libraries. No hdf5 file, and no termination was seen.

I have no idea to how to make arma-12.6.4 work with hdf5, either locally or remotely. While arma-11.4.4 runs locally, please help me to how to run it on cluster with hdf5. Everything else (diagonalization and arma computations) runs well in both and all cases. I guess conda hdf5 installation has some issue, along with arma-12.6.4 version.

我不知道如何让ARMA-12.6.4在本地或远程与HDF5一起工作。当ARMA-11.4.4在本地运行时，请帮助我如何使用hdf5在集群上运行它。其他一切(对角化和ARMA计算)在这两种情况下都运行得很好。我猜Conda hdf5安装以及ARMA-12.6.4版本都有一些问题。

Edit: Thanks to @mtall I was able to clear the local version problem. The issue in the cluster is still unclear. I have updated the question accordingly

编辑：多亏了@mall，我才能清除本地版本问题。该集群中的问题仍不清楚。我已相应地更新了问题

The documentation states: "HDF5 support can be enabled by defining ARMA_USE_HDF5 before including the armadillo header" and "link with the HDF5 library".

文档声明：“可以通过在包含Aradillo头文件之前定义ARMA_USE_HDF5来启用对HDF5的支持”和“与HDF5库链接”。

So try the following in your program:

因此，在您的程序中尝试以下操作：

#define ARMA_USE_HDF5
#include <armadillo>

Then add -lhdf5 to your compiler flags. For example:

然后将-lhdf5添加到您的编译器标志。例如：

g++ program.cpp -o program -O2 -larmadillo -lhdf5

If the HDF5 library is not in the regular system path, you will need to specify the path using the -L flag.

如果hdf5库不在常规系统路径中，则需要使用-L标志指定路径。