#RSHELL-MAT - Shell-based distributor for Matlab

Content

• Short description
• Platforms and other requirements
• Quick Start
• Main workflow description
• Customizing and running your own Distributor
  • List of parameters
  • Providing your custom functions for split, merge and kernel
  • LOG files
• Notes and tutorials
  • Setting up SSH pubkey authentication
  • Setting up SSHD server using Cygwin on Windows
  • Enabling arcfour cipher (for Windows SSHD)
  • Specifics
  • Other info
  • Performace examples
• Contact

Short description

rshell-mat is bash script based project that helps to ease heavy data processing in Matlab. Its main idea is to send the split data to several remote servers and run the most heavy computations simultaneously using those remotes. When the processing is done, the split result files are copied back to the local machine, merged by using the user-provided function; so the data can be used further in Matlab. The processing is done by the bash scripts and a Matlab class:

• dhead.sh - a local head script that performs the distribution among the remote servers and also copying all the files forward and back
• dserver.sh - a remote server script that launches Matlab function on the remote server
• dtest.sh - a tester script that checks connectivity to the remotes and clears all the previous data inside the remote working directory
• dscp.h - a script to copy any additional .m files such as Matlab functions and classes that are necessesary for computaions on remotes
• dtransfer.sh - a script that copies any additional heavy data, in .dat format; the script is optionally used and works only and directly with CachedNDArray matlab class
• Distrubutor.m - is a handle Matlab interface, that coordinates data initialization, splitting, script launching and merging. Note: the split, kernel and merge functions must be provided by user, as well as the initialized structures for each method.

Platforms

The scripts are able to distribute the data processing among Linux servers, Windows (Cygwin SSHD) servers and the mixture of both. SSH connection is used for all the processing. As for the head (local) machine, it must be Linux, since we could not find a way to make it work within Cygwin due to impossibility to tie up the ssh-agent, ssh-add and a Matlab process.

Quick start

The Matlab interface class called Distributor.m manages the usage of all the Bash scripts. A Matlab example is provided - two vector summation. To run the example, you can use test_distributor.m with the following steps:

• IMPORTANT: it is necessary to set up the login process through the SSH public-key, otherwise, the password prompts will not allow for the programm to continue (see Notes for tutorial examples).
• Before launching the Matlab, set up the SSH connection to the remotes by using ssh-agent. For example, run the following commands in your command line (it must be Linux environment):

eval `ssh-agent`
ssh-add

and provide the pass-phrase.

• Now launch the Matlab from the same terminal command line, not in the background:

matlab

• Open the example script test_distributor.m.
• Inside the example Matlab script, insert your own settings for the remote servers (such as IP addresses, login, paths, etc). Your Matlab script is now ready to be run, since the split, kernel and merge functions are provided for the example.
• When all the calculation are finished and you no longer wish to use the SSH connection and Matlab, exit Matlab, and do not forget to remove the added key (run in a command line):

kill $SSH_AGENT_PID

Main workflow of the Distributor processes

These are the main steps that happen inside when the Distributor is run:

• Initialization:
  • Connectivity tests for each of the remotes
  • Creating / clearing the remote workfolder for each of the remotes (all files are deleted)
  • Creating / clearing the local workfolder (*.log and result*.mat files are deleted)
  • Internal variable initialization
• Copying any additional files to the remotes (optional step):
  • Copying of the additional Matlab functions or classes that will be used during remote computations (note: the wrapper function file is not included in this step)
  • Copying of any additional .dat files that are tied to CachedNDArray Matlab data class
• Running the distributor:
  • Splitting the data - is done based on the user-provided function and input
  • Distribution - which automatically copies saved by user .mat files to the corresponding remotes, launching the Matlab wrappers on the remotes, waiting for the results and then copying back the generated result .mat files to the local workfolder
  • Merging the obtained data in order to use it further in Matlab local session

Customizing and running your own Distributor

Main interface

Use the following steps to run your Distributor:
Distributor variable declaration by running a constructor (see what are the input parameters in parameter list)

d = Distributor(login, path_rem, ipaddrs, path_vars, vars, ...
    path_curr, sleeptime, path_res, printout);

Obtain function handles on your split, kernel and merge functions, as well as initialize the input structures for each of these functions (see functions signatures):

in_split = struct('field1', val1, 'field2', val2, ...);
in_merge = struct('field1', val1, 'field2', val2, ...);
h_split = @split;
h_kernel = @kernel;
h_merge = @merge; 

Launch the Distributor:

out_merge = d.launch(h_split, in_split, h_kernel, h_merge, in_merge);

Use the output variable\structure further in your Matlab code:

val1 = out_merge.field1;
val2 = out_merge.field2;
...

List of parameters

login is a login id for the remotes (assumed the same for all the remotes), in a string format, e.g.: login = 'remote_user';.

path_rem is a workdirectory path on remote servers (note: if the folder does not exist, it will be created during the initialization; if the folder exists all the containing data will be cleared); path = '/home/remote_user/tmp'.

ipaddrs is a list of IP addresses, in a string format; it has a form of ['ipaddrs1' ' ' 'ipaddrs2' ' ' ...] - each IP address must be separated by one space character, like this: ' ', from its neighbors.

path_vars is a folder path where all the vars data (Matlab worspace variables, normally in format *.mat) is stored and loaded from.

vars is a root name of temporal files where the work variables are saved to, in a string format. An example of the saved file with a vars rootname will be {vars1.dat, vars2.dat, ...}; each of these files will be kept on server1, server2, ....

path_curr is a folder path where the .sh scripts are located, for the Mandelbrot example case it is a full path to the current folder.

sleeptime is a pause interval in seconds, integer, it is used inside dhead.sh to wait until the tasks are finished on remotes; you may want to increase it for heavier computations. This variable insures the processes are not stuck in an infinite loop on the remotes.

path_res is a name of a temporal folder on local machine where the result files will be copied to, in a string format.

printout is a boolean (0 or 1) variable that allows (1) or suppresses (0) any printf and echo bash outputs to the Matlab command line (in case of suppresion the outout is forwarded to a *.log file). Note that for big repetitive data computations, once the code is debugged, it is advised to turn the direct output off for faster processing time.

Providing your custom functions for split, merge and wrapping (kernel)

These are the signatures of three functions that user must provide for their Distributor:

• function output = split(input)
• function output = kernel(file_mat, res_fname, cache_vname, ncache), note: cache parameters could be omitted, e.g: kernel(file_mat, res_fname, ~, ~) if you do not use any supplemental .dat files in computations
• function output = merge(input)

The split and merge functions have their own input and output variables which are the Matlab struct data types that contain the necessary variables as fields.

Certain input fields must be passed and used correctly for any split or merge functions. The necessary parameters for any split function:

% to be called from main script before we launch the distributor
in_split = struct('ncluster', d.ncluster, 'path_vars', d.path_vars, ...);

where d is out distributor. With these passed parameters, the skeletone of the split function has the next form:

% user provided function which is called automatically inside launch()
function out = split(input)
    ...
    % for each cluster
    for i = 1 : input.ncluster
        % split each variable into chunks
        ...
        % save each chunk and any other variables
        % for the corresponding remote 'i'
        save([input.path_vars, input.vars int2str(i) '.mat'], ...);
    end
% indicate the operation is done
out = 1;
end

The merge mandatory parameters are:

% to be called from main script before we launch the distributor
in_merge = struct('ncluster', d.ncluster, 'path_res', d.path_res, 'vars', d.vars, ...);

where d is our distributor. With these passed parameters, the skeletone of the merge function has the next form:

% user provided function which is called automatically inside launch()
function out = merge(input)
...
% for each cluster file
for i = 1 : input.ncluster 
    % load the corresponding result file
    load([input.path_res 'result_' input.vars int2str(i) '.mat']);
    % save it to the result variable
    ...
end
% save the result variable to the output structure
out = struct(...);

The kernel function have two or four variables as input: file_mat is a .mat filename where Matlab workspace variables are kept; res_fname is a filename where the result will be written to for the current remote (string format); and cache_vname together with ncache are for indication a rootname of .dat file where Matlab cache variable is stored and the number of such files (the last two parameters might be ommited).

The necessity to have .dat files might not be obvious, but we use rshell-mat in conjunction with CachedNDArray data structure for our cryo3D project (see Notes for more details), for that reason we figured out that not all the data can be stored and transferred as .mat file, but in case if there is any other disk data, it could be transferred and used as a .dat file.

The example of the basic merge function:

function out = kernel(file_mat, res_fname, ~, ~)
% load the corresponding variables into Matlab workspace
load(file_mat);
% operate the variables
...
% save the result variable to the provided name
save(res_fname, ...);
% indicate the operation is done
out = 1;
end

Note, you can always perform an output when debugging your kernel function by doing fprintf(). The output will be printed into a corresponding log (.err or .out) file on the remote workfolder. An example:

function out = kernel(file_mat, res_fname, ~,~)
load(file_mat);
if (size(var)>x)
    fprintf('The size of var is %i\n', size(var));
    fprintf('x is %f\n', x);
    ...
end
...
end

In the sample code above, if the if condition met, two messages will be print into .out log file. Further on, if an error occurs, the error message will be printed to the .err log file. Both of the log files are kept on the corresponding remote workspace directory.

LOG files

For each Matlab and Bash process, there is a corresponding *.log file that allows to check the running status and/or for any errors that can occur - whether on local or remote machines. The list of .log files is as follows:

• Local log files
  • tester.log - generated on local machine when user choses to suppress any distributor output; it contains the information on ssh connectivity and accessibility to remotes
• transfer.log - generated on local machine when user choses to suppress any distributor output; it containds the information on performance of .dat and .m files transfers
  • remmat.log - with remmat being defined by user (see Parameter List for details) - generated on a local machine when user choses to suppress any distributor output; it contains the progress steps of the dhead.sh Bash launcher, i.e. .mat data transfer, Matlab remote launching, output data transfer, etc.
• Remote log files
  • *.out - generated on each of the remote machines, it contains information on the dserver.sh steps, as well as Matlab wrapper output (if any was provided by a user)
  • *.err - generated on each of the remote machines, it only contains error messages that caused dserver.sh or Matlab wrapper to stop

Notes

Setting up SSH public-key authentication

The distribution scripts assume all the remote machines have the same login id and are accessed by using public key authorization (pass phrase); for full step-by-step refer to a tutorial on How do I set up SSH public-key authentication to connect to a remote system. Here we list a brief description of the procedure:

• On the local maching (what is intended to be a head), generate public and private keys by running the command:
  $ ssh-keygen -t rsa
  • Provide a filename (press to save it as default - id_rsa.pub, recommended) and a passphrase (press to not use any pass phrase, not recommended)
• Copy id_rsa.pub to each of the remotes, e.g. by sftp - this is your public key
• For each of the remotes do following:
  • Login to the remote, e.g. by using ssh connection: ssh remote_user@remote_ip
  • Copy your public key to the authorized keys cat id_rsa.pub >> ~/.ssh/authorized_keys
  • Set the correct priveleges:

chmod 600 ~/.ssh/authorized_keys  
chmod 700 ~/.ssh  

• Logout of the remote and go back to the local machine: exit
• Now you can test the ssh connection by a simple ssh command or by using ssh-agent (you are supposed to use ssh-agent for distributor anyway):

eval `ssh-agent`
ssh-add  

• Make sure there is no password prompt, but a pass-phrase prompt instead
• Remove the ssh-agent after exiting

kill $SSH_AGENT_PID

Setting up SSHD server using Cygwin on Windows

When using a Windows machine as a SSHD server, it is necessary to install and configure Cygwin: Cygwin - SSHD Configuration(it is a step-by-step tutorial with other additional information). Here, the main steps are described briefly (the steps will require administration rights and will ask to reboot the system at the end):

• Install Cygwin on Windows; when installing make sure to include the following packages: cygrunsrv, openssh (you can find them by using search).
• Edit Path variable on Windows, append the following string: ";c:\cygwin\bin" (the path where Cygwin is installed) and click OK.
• Chose a username for the server (new user will be created on your Windows machine); for the distributor chose the same username as for all of your other remote machines.
• Create a new user with the chosen username on Windows.
• Run Cygwin as administrator.
• Type the following commands / answers:
  • ssh-host-config
  • yes to privilege separation
  • yes to install sshd as a service
  • [] empty for value of CYGWIN for the daemon
  • yes to use a different name
  • username for the new username, e.g. cryo
  • username to reenter
  • password enter the password for the username (must be the same on all machines that distributor will use); reenter
• Setup Local Security Authority (LSA) by running:
  • cyglsa-config
  • Answer yes to all of the questions
• The last operation will automatically reboot the system
• Sometimes it is necessary to edit etc/sshd_config file and set to yes the following attributes:
  • X11Forwarding
  • RSA Authentication
  • Publickey Authentication
  • Append to the end of the file: Allow users and the username you are planning to login from, e.g. cryo
• Before restarting the sshd server, you may need to include the arcfour cipher to the configuration file (see the next section)
• The changes will take place after restarting the sshd service:
  • net stop sshd
  • net start sshd
• The last important step is to configure your Windows 7 firewall options, so that it allows for incoming connections. The full tutorial can be found: Configure Windows Firewall. The brief steps are listed below:
  • open Windows Firewall
  • "Inbound Rules" -> "Actions" -> "New Rule"; press "Next"
  • chose "Port"; press "Next"
  • chose "TCP" and "Specific local ports" and enter the port number (if during sshd installation you chose a specific number, enter it; otherwise, enter "22" as a default); press "Next"
  • chose "Allow the connection"; press "Next"
  • check all the boxes "Domain", "Private", "Public"; press "Next"
  • give it a name and description, e.g. "sshd" and "open port 22"; press "Next"
  • press "Finish"

Enabling arcfour cipher (for Windows)

The distributor uses an arcfour cipher to compress the transferred data. You can always remove its usage by editing the dhead.sh file. Otherwise, Cygwin does not allow this cipher by default, therefore, we need to include it manually (normally, you do not have to do anything for Linux). Enabling is done by followind the steps:

• First make sure the cipher is available on the current machine, type in Cygwin:
  ssh -Q cipher localhost | paste -d , -s
  It will list all the available ciphers of the system. Make sure arcfour is in the list.
• Edit the config file vim etc/sshd_config (or etc/ssh/sshd_config) to include the cipher by adding the line:
  Ciphers arcfour
• Restart the sshd server:

net sshd stop  
net sshd start  

Specifics

The Distributor clears all the data in the provided work folder on each of the remotes, so make sure it is either new folder or you do not have any valuable data in the work directories on each of the remotes. The working folder will be created directly inside the $HOME directory of the user on each of the remotes.

Other

The bash distributor package was created as a part of cryo3d Matlab-based software developed at Yale IPAG which reconstructs a 3D protein model from cryogenic particle images. rshell-mat was developed to deal with the heaviest computational part of the pipeline - calculations of SSDs to find the best projection direction and transfomation parameters.

Performance examples

Two examples will be considered. The first is the provided Mandelbrot calculation; the second is the usage within the cryo3d software package for SSDs calculations.

Cryo3d-whole-pipeline performance when using single head server vs. the same head and two remotes (the most expensive operations will be distributed between those two remotes) vs the same head and four* remotes. Different database sizes provided (the larger the more computationally expesive it is) and a sampling angle theta (the smaller the more computationally expensive it is).

---------------------------------------------------------------------------------   
Test params / Configuration   |  Single machine   |  Cluster of two / four * 
---------------------------------------------------------------------------------   
15K database, theta = 12      |      1.3  hrs     |     0.94  hrs  /  1.1   hrs   
15K database, theta = 6       |      5.0  hrs     |     3.45  hrs  / --
15K database, theta = 3       |      29   hrs     |     21    hrs  / --
---------------------------------------------------------------------------------   
67K database, theta = 12      |      2.8  hrs     |     2.1   hrs  /  1.65  hrs  
67K database, theta = 6       |      12   hrs     |     7.8   hrs  /  6.2   hrs  
67K database, theta = 3       |     ~150  hrs     |     -.-   hrs  / ~50    hrs  
--------------------------------------------------------------------------------   

* All of the four remotes had different memory and GPU characteristics, therefore, some performed slower than others which means the performance numbers could be even better than displayed.

For questions and inqueries

Victoria Rudakova, vicrucann(at)gmail(dot)com