Using SSD Disk Space

Introduction

We have added SSDs, solid state disks, on a few compute nodes:

• These are local fast disks that can speed up applications that preform a lot of intensive I/O operations,
• hence such jobs should complete faster when using SSDs.
• Jobs that do not perform intensive I/Os should not use the SSDs - these are a scarce shared resource.
  
  
• Since these disks are local to the compute nodes: 
  
  • you cannot see the SSD from either login nodes,
  • your job will be able to use the SSD only while the job is running, hence
  • you need to prepare the files needed for a job prior to submitting the job, using /pool or /scratch, and

  • request the right amount of SSD disk space:

    • this is the maximum amount of disk space your job will need on the SSD at run-time, (similarly to the maximum of memory it will need).

  • Your job script will have to 
    
    • copy those files to the SSD before processing them,
    • adapted to use the SSD,
    • upon completion, copy the results from the SSD elsewhere (/pool or /scratch),
    • and delete what you wrote on the SSD.
  • If you exceed the amount of disk space, i.e., your quota, your job won't be able to write any longer to the SSD, 
    hence your job script should stop when encountering such error.

 Since we have only a few of them, they should be used only if your application greatly benefit from using an SSD.

 The SSDs are currently only available for the uTxlM.rq queue, a restricted queue. Contact Paul or Sylvain if you want to be authorized to use it.

HowTo

Since you can't access the SSDs from a login node, you must prepare the data the job will need somewhere else, like on /pool (or on /scratch) before submitting a job.

Like for memory, you need to guestimate how much SSD space your job will need. You will not be able to use more SSD space than you requested.

 Remember, your job will still be able to access the /home, /data, /pool and /scratch disks, hence you don't have to copy everything on the SSD,

only the I/O intensive part of the analysis should use the SSD.

How to Prepare my Data

1. Create a subdirectory in /pool (or /scratch) and move or copy the data you will need, for example (as user smart1)
   cd /pool/genomics/smart1
mkdir -p great/project/wild-cat
   Now is have a directory for this case, and would copy the I/O intensive part of the required data set in it.
   
   
2. While not required, you can pack these data in a compressed tar-ball
   cd /pool/genomics/smart1/great/project/wild-cat
tar cfz ../wild-cat.tgz .
   The file /pool/genomics/smart1/great/project/wild-cat.tgz now holds you input data set, 
   being compressed it is likely to be smaller than the content of /pool/genomics/smart1/great/project/wild-cat,
   That directory can be deleted, unless you will need it later.
   
   
3. Ancillary data and/or configuration files that are not causing intensive I/O can stay on a location under /pool (or /scratch)

How to Adjust my Job Script to Use the SSD

1  Copy the Data to the SSD

• • At the top of your job script, load the tools/ssd module and copy or extract your data set as follows:

example using recursive copy

or
example using compressed tar-ball

 The advantage of the compressed tar-ball is that the .tgz file is likely to be smaller than the content of the directory, 

hence less I/O transfer from the /pool disk, while un-compressing and writing  to the SSD is fast,

2 Adjust the Script or a Configuration File

• You need to replace all references to /pool/genomics/smart1/great/project/wild-cat by $SSD_DIR,
• this can be easily done at the shell script level, but not in a configuration file, i.e., for flags/options
    execute -o /pool/genomics/smart1/great/project/wild-cat/result.dat
  is replaced by
    execute -o $SSD_DIR/result.dat
  
  
• Here is a simple trick to modify a configuration file:

  • Let's assume that your analysis uses a file wow.conf, where for instance the full path of some files must be listed, like:
    wow.conf

    # this is the configuration file of the fabulous WOW package input=/pool/genomics/smart1/great/project/wild-cat/wiskers.dat output=/pool/genomics/smart1/great/project/wild-cat/tail.dat paws=4 eyes=2

  • Replace the wow.conf file by a wow.gen file as follows:
    wow.gen

    # this is the configuration file of the fabulous WOW package input=XXXX/wiskers.dat output=XXXX/wild-cat/tail.dat paws=4 eyes=2

  • And create the wow.conf file from the wow.gen at run-time by adding the following in the job script:

    sed "s=XXXX=$SSD_DIR=" wow.gen > wow.conf

    As long as XXXX is not used for anything else, this will replace every occurrence of XXXX by the value of the environment variable SSD_DIR.


3 Run the Analysis

• With your data copied to the SSD and with your commands and configuration files adjusted to use the SSD, run your analysis.

4 Copy the Results from the SSD

• At the end of the job script, you must add instruction to copy the results of your analysis back to /pool(or /scratch, or /data).

  
  If/when the results are easily identifiable, you can use the commands mv or tar,  and find, here are a few examples:

  
  

  1. Move the directory where all the results are stored and the log file, delete the rest.
     moving identifiable results, delete the rest

     # move results and log file back cd $SSD_DIR mv results /pool/genomics/smart1/great/project/wild-cat/. mv wow.log /pool/genomics/smart1/great/project/wild-cat/. # # delete the rest rm -rf *

  2. Move the directory where all the results are stored and the log file, delete the input (conservative approach, in case you missed something).
     moving identifiable results, delete known input sets

     # move results and log file back cd $SSD_DIR mv results /pool/genomics/smart1/great/project/wild-cat/. mv wow.log /pool/genomics/smart1/great/project/wild-cat/. # # delete input set and other stuff rm -rf input rm wow.gen wow.conf

  3. Move using the --update flag of mv (see man mv)
     moving using --update

     # move results using --update cd $SSD_DIR mv --update * /pool/genomics/smart1/great/project/wild-cat/. # # delete the rest rm -rf *

      Note, you can use mv --update on an explicit list (of files, directories, or file specification), not just * (everything), and

     you do not have to remove the rest, but can only remove what you know you can safely remove (conservative approach).
      

  4. Find newer files and move them: the trick is to create a 'timestamp' file before starting the analysis.
      That file can be used later to find any newer file with the --newer= option of tar (see man tar):
     Using a timestamp file and tar --newer=

     # set the timestamp date > $SSD_DIR/started.txt # run the analysis ... # copy the new files in the subdir data/ to a compressed tar-ball cd $SSD_DIR tar --newer=$SSD_DIR/started.txt -cfz /pool/genomics/smart1/great/project/wild-cat-results.tgz data/ # now remove it rm -rf data/ # etc... # delete everything, unless rm -rf *

     See previous comments and what to tar and what to remove: once you've tar'd new stuff in data/,  remove data/, etc..

  5. Using the timestamp file and  the find command (see man find):
     Using find and a timestamp file

     # set the timestamp date > $SSD_DIR/started.txt # run the analysis ... # find the new files in the subdir data/ cd $SSD_DIR find data/ -newer $SSD_DIR/started.txt -type f > /tmp/list # do the same on logs/, append to the list find logs/ -newer $SSD_DIR/started.txt -type f >> /tmp/list # etc... # now save what is in the list with one tar tar --files-from=/tmp/list -cfz /pool/genomics/smart1/great/project/wild-cat-results.tgz data/ # now remove data/ and logs/ rm -rf data/ logs/ # etc... # delete everything, unless rm -rf *

      There are many more ways to accomplish this ....

      BTW, the advantage of writing a .tgz file, rather than moving files is two fold, assuming your stuff is compressible:

     1. You write less in the .tgz file, so it should be done faster (reading and compressing should be fast, writing is the slow step)

     2. you need less disk space for your output (since it is compressed).

The drawback being that you need to know how to handle/view/deal with a .tgz file.

A Pseudo Example

Here is what a job script might look like:
Pseudo Example

Last Updated 17 Jul 2019 SGK