Managing I/O on TACC Resources
Last update: June 8, 2023
The TACC Global Shared File System, Stockyard, is mounted on nearly all TACC HPC resources as the
$WORK) directory. This file system is accessible to all TACC users, and therefore experiences a huge amount of I/O activity (reading and writing to disk) as users run their jobs. This document presents best practices for reducing and mitigating such activity to keep all systems running at maximum efficiency for all TACC users.
I/O stands for Input/Output and refers to the idea that for every input to a computer (keyboard input, mouse click, external disk access), there is an output (to the screen, in game play, write to disk). In the HPC environment, I/O refers almost exclusively to disk access: opening and closing files, reading from, writing to, and searching within files. Each of these I/O operations (iops),
close, access each file system's MetaData Server (MDS). The MDS coordinates access to the
/work file system for all users. If a file system's MDS is overwhelmed by a user's I/O workflow activities, then that file system could go down for an indeterminate period and all current jobs on that resource may fail.
Examples of intensive I/O activity that could affect the system include, but are not limited to:
- reading/writing 100+ GBs to checkpoint or output files
- running with 4096+ MPI tasks all reading/writing individual files
- Hundreds of concurrent Python jobs, especially those using multiple modules such as
As TACC's user base continues to expand, the stress on the resources' shared file systems increases daily. TACC staff now recommends new file system and job submission guidelines in order to maintain file system stability. If a user's jobs or activities are stressing the file system, then every other user's jobs and activities are impacted, and the system admins may resort to cancelling the user's jobs and suspending access to the queues.
If you know your jobs will generate significant I/O, please submit a support ticket and an HPC consultant will work with you.
Consider that your
$WORK) directories are for storage and keeping track of important items. The
$WORK file system is intended to be an area where you can build your code, store your input and output data, and any intermediate results. The
$WORK fileystem is not designed to handle jobs with large amounts of I/O load or iops.
Actual job activity, reading and writing to disk, should be offloaded to your resource's
$SCRATCH file system. You can start a job from anywhere but the actual work of the job should occur only on the
$SCRATCH partition. You can save original items to
$WORK so that you can copy them over to
$SCRATCH if you need to re-generate results.
Table 1 outlines TACC's new recommended guidelines for file system usage.
|cron jobs, scripts and templates, environment settings, compilations
$HOME directory is backed up
|software installations, original datasets that can't be reproduced.
|The Stockyard file system is NOT backed up.
Ensure that your important data is backed up to Ranch long-term storage.
|Reproducible datasets, I/O files: temporary files, checkpoint/restart files, job output files
|Not backed up.
$SCRATCH file systems are subject to purge if access time 2 is more than 10 days old.
2 The operating system updates a file's access time when that file is modified on a login or compute node. Reading or executing a file/script on a login node does not update the access time, but reading or executing on a compute node does update the access time. This approach helps us distinguish between routine management tasks (e.g.
scp) and production use. Use the command
ls -ul to view access times.
Here we present guidelines aimed at minimizing I/O impact on all TACC resources. Primarily, this means redirecting I/O activity away from Stockyard (the
$WORK file system) onto each resource's own local storage: usually the respective
$SCRATCH file systems.
Your jobs are run on the compute nodes of each resource and each compute node has a local
/tmp directory on it. You can use the
/tmp partition to read/write temporary files that do not need to be accessed by other tasks. If this output data is needed at the end of the job, the files may be copied from
/tmp to your
$SCRATCH directory at the end of your batch script. This will greatly reduce the load on the file system and may provide performance improvement.
Data stored in the
/tmp directory is as temporary as its name indicates, lasting only for the duration of your job. Each MPI task will write output to the
/tmp directory on the node on which it is running. MPI tasks cannot access data from
/tmp on different nodes. Each TACC resource's compute nodes host a different amount of
/tmp space as shown in Table 2 below. Submit a support ticket for more help using this directory/storage.
|Storage per Compute Node
|107 GB normal/large
32 GB development
Each TACC resource has its own Scratch file system,
/scratch, accessible by the
$SCRATCH environment variable and the
Scratch file systems are not shared across TACC production systems but are specific to one resource. Scratch file systems have neither file count or file size quotas, but are subject to periodic and unscheduled file purges should total disk usage exceed a safety threshold.
TACC staff recommends you run your jobs out of your resource's
$SCRATCH file system instead of the global
$WORK file system. To run your jobs out of
$SCRATCH, copy (stage) the entire executable/package along with all needed job input files and/or needed libraries to your resource's
Compute nodes should not reference the
$WORK file system unless it's to stage data in or out, and only before or after jobs.
Your job script should also direct the job's output to the local scratch directory:
# stage executable and data
cp $WORK/myprogram testrunA
cp $WORK/jobinputdata testrunA
# launch program
ibrun testrunA/myprogram testrunA/myinputdata > testrunA/output
# copy results back permanent storage once job is done
cp testrunA/output $WORK/savetestrunA
If your program regularly writes data to disk from each process, for instance for checkpointing, avoid writing output to a separate file for each process, as this will quickly overwhelm the Metadata Server. Instead, employ a library such as
netcdf to write a single parallel file for the checkpoint. A one-time generation of one file per process (for instance at the end of your run) is less serious, but even then you should consider writing parallel files.
Alternatively, you could write these per-process files to each compute node's
/tmp directory, see below.
Jobs that have multiple tasks that read and/or write to the same file will often suspend the file in question in an open state in order to accommodate the changes happening to it. Please make sure that your I/O activity is not being directed to a single file repeatedly. You can use
/tmp on the node to store this file if the condition cannot be avoided. If you require shared file operations, then please ensure your I/O is optimized.
If you anticipate the need for multiple nodes or processes to write to a single file in parallel (aka single file with multiple writers/collective writers), please submit a support ticket for assistance.
If you are close to file quota on either the
$HOME file system, your job may fail due to being unable to write output, and this will cause stress to the file systems when attempting to write beyond quota. It's important to monitor your disk and file usage on all TACC resources where you have an allocation.
Monitor your file system's quotas and usage using the
taccinfo command. This output displays whenever you log on to a TACC resource.
Principal Investigators can monitor allocation usage via the TACC User Portal under "Allocations->Projects and Allocations". Be aware that the figures shown on the portal may lag behind the most recent usage. Projects and allocation balances are also displayed upon command-line login.
To display a summary of your TACC project balances and disk quotas at any time, execute:
login1$ /usr/local/etc/taccinfo # Generally more current than balances displayed on the portals.
---------------------- Project balances for user <user> ----------------------
| Name Avail SUs Expires | Name Avail SUs Expires |
| Allocation 1 | Alloc 100 |
------------------------ Disk quotas for user <user> -------------------------
| Disk Usage (GB) Limit %Used File Usage Limit %Used |
| /home1 1.5 25.0 6.02 741 400000 0.19 |
| /work 107.5 1024.0 10.50 2434 3000000 0.08 |
| /scratch1 0.0 0.0 0.00 3 0 0.00 |
| /scratch3 41829.5 0.0 0.00 246295 0 0.00 |
Manipulate data in memory instead of files on disk when necessary. This means:
- For unimportant data that do not need a backup, process that data directly in memory.
- For any commands in the intermediate steps, process those commands directly in memory instead of creating extra script files for them.
TACC staff has developed OOOPS, Optimal Overloaded I/O Protection System, an easy-to-use tool to help HPC users optimize heavy I/O requests and reduce the impact of high I/O jobs. If your jobs have a particularly high I/O footprint, then you must employ the OOOPS tool to govern that I/O activity.
Employing OOOPS may slow down your job significantly if your job has a lot of I/O.
The OOOPS module provides two functions
set_io_param_batch for single-node jobs and multiple-node jobs, respectively. These commands adjust the maximum allowed frequency of
stat function calls on all compute nodes involved in a running job. Execute these two commands within a Slurm job script or within an
These functions instruct the system to modulate your job's I/O activity, thus reducing the impact on the designated file system. For both functions, use "0" to indicate the
$SCRATCH file system and "1" to indicate the
$WORK file system.
These indices are subject to change. See each command's
help option to ensure correct parameters:
c123-456$ set_io_param -h
Indicate the frequency of
stat function calls, from the least to the most, with
First, load the
ooops module in your job script or
idev session to deploy OOOPS. Next, set the frequency of I/O activities using either the
|Job Script Example
|Interactive Session Example
To turn off throttling on the
$SCRATCH file system for a submitted job, run the following command on a login node or within an
idev session while the job is running:
login1$ set_io_param 0 unlimited
|Job Script Example
|Interactive Session Example
To turn off throttling on the
$SCRATCH file system for a submitted job, you can run the following command (on a login node) after the job is submitted:
login1$ set_io_param_batch [jobid] 0 unlimited
If OOOPS finds intensive I/O work in your job, it will print out warning messages and create an
stat call report after the job finishes. To enable reporting, load the OOOPS module on a login node, and then submit your batch job. The reporting function will not be enabled if the module is loaded within a batch script.
For jobs that make use of large numbers of Python modules or use local installations of Python/Anaconda/MiniConda, TACC staff provides additional tools to help manage the I/O activity caused by library and module calls.
On Stampede2 and Frontera: Load the
python_cacher module in your job script:
module load python_cacher
This library will cache python modules to local disk so python programs won't keep pulling the modules over and over from the
/work file systems.
python_cacher does not work, you can copy your Python/Anaconda/MiniConda directory to the local
/tmp directory of each involved compute node for a specific job.
Stampede2 and Frontera: To track the full extent of your I/O activity over the course of your job, you can employ another TACC tool,
iomonitor that will report on
stat() calls during your job's run. Place the following lines in your job submission script after your Slurm commands, to wrap your executable:
Log files will be generated during the job run in the working directory with prefix
Note: Since the
iomonitor tool may itself generate a lot of files, we highly recommend you profile your job beginning with trivial cases, then ramping up to the desired number of nodes/tasks.
Please feel free to submit a support ticket if you need any further assistance.
Try to avoid
$WORK for production jobs
$SCRATCH for production jobs
Take advantage of the local
/tmp space if possible
|Keep thousands of files in on a single directory
|Create subdirectories and keep files in separate subdirectories
|Work with many tiny files
|Work with large files if possible
Use the local
/tmp space if possible
|Create files on disk for unnecessary data or commands
|Process data that do not require a backup directly in memory
Process intermediate commands directly in memory instead of creating additional script files
|Use a single stripe for large files
|Use a single stripe for small files
Stripe large files on the Lustre file systems
|Conduct open/close/state operations repetitively
|Open/close only once for each file if possible
Reduce the state call frequency if possible
|Use many processes to work simultaneously on the same file
|Use scalable Parallel I/O libraries, like phdf5, pnetcdf, PIO
Limit the number of processes for I/O work (one processor per node is a good start)
Make copies of the required files in advance when necessary
|Perform high frequency I/O work
|Keep the data in memory if possible
Reduce the frequency of the I/O work
Limit the number of concurrent jobs
Take advantage of OOOPS
|Perform large-scale runs with R/Python on
|Install Python/R modules under
Copy Python/R modules under
/tmp for large-scale runs Use Python_Cacher
|Overlook I/O pattern and I/O workload
|Use profilers or I/O monitoring tools when necessary