Data storage

In this tutorial, we go over places to store data on Triton and how to access it remotely.

Basics

Triton has various ways to store data. Each has a purpose, and when you are dealing with the large data sets or intensive IO, efficiency becomes important.

Roughly, we have small home directories (only for configuration files), large Lustre (scratch and work, large, primary calculation data), and special places for scratch during computations (local disks). At Aalto, there is aalto home, project, and archive directories which, unlike Triton, are backed up but don’t scale to the size of Triton.

A file consists of its contents and metadata. The metadata is things like user, group, timestamps, permissions. To view metadata, use ls -l or stat.

Filesystem performance can be measures by both IOPS (input-output operations per second) and stream I/O speed. /usr/bin/time -v can give you some hints here. You can see the profiling page for more info.

Think about I/O before you start! - General notes

When people think of computer speed, they usually think of CPU speed. But this is missing an important factor: how fast can data get to the CPU? In very many cases, input/output (IO) is the true bottleneck and must be considered just as much as processor speed. In fact, modern computers and especially GPUs are so fast, it is very easy for a few GPUs with bad data access patterns to bring the cluster down for everyone.

The solution is similar to how you have to consider memory: there are different types of filesystems with different tradeoffs between speed, size, and performance, and you have to use the right one for the right job. Often times, you have to use several in tandem: for example, store original data on archive, put your working copy on scratch, and maybe even make a per-calculation copy on local disks.

Consider:

  • How much IO in the first place? Do you continually re-read the same data?
  • What’s the pattern of it, and which filesystem is best for it? If you read all at once, scratch is fine, but if there are many small files or random access, local disks may help.
  • Do you write log files / checkpoints more often than is needed?
  • Some programs use local disk as swap-space. Only turn on if you know it is reasonable.

There’s a checklist in the storage details page.

Avoid many small files! Use a few big ones instead. (we have a dedicated page on the matter)

Summary table

Name Path Quota Backup Locality Purpose
Home $HOME or /home/$username/ hard quota 10GB Nightly all nodes Small user specific files, no calculation data.
Work $WRKDIR or /scratch/work/$username/ 200GB and 1 million files x all nodes Personal working space for every user. Calculation data etc. Quota can be increased on request.
Scratch /scratch/$dept/$project/ on request x all nodes Department/group specific project directories.
Local temp /tmp/ limited by disk size x single-node Primary (and usually fastest) place for single-node calculation data. Removed once user’s jobs are finished on the node.
Local persistent /l/ varies x dedicated group servers only Local disk persistent storage. On servers purchased for a specific group. Not backed up.
ramfs (login nodes only) $XDG_RUNTIME_DIR limited by memory x single-node Ramfs on the login node only, in-memory filesystem

Home directories

The place you start when you log in. For user init files, some small config files, etc. No calculation data. Daily backup. Usually you want to use scratch instead.

scratch and work: Lustre

This is the big, high-performance, 2PB Triton storage. The primary place for calculations, data analyzes etc. Not backed up but is reliable against hardware failures (RAID6, redundant servers), but not safe against human error.. It is shared on all nodes, and has very fast access. It is divided into two parts, scratch (by groups) and work (per-user). In general, always change to $WRKDIR or a group scratch directory when you first log in and start doing work.

Lustre separates metadata and contents onto separate object and metadata servers. This allows fast access to large files, but a larger overhead than normal filesystems. See our info on small files.

See Storage: Lustre (scratch)

Local disks

Local disks are on each node separately. For the fastest IOs with single-node jobs. It is cleaned up after job is finished. Since 2019, things have gotten a bit more complicated since our newest (skl) nodes don’t have local disks. If you want to ensure you have local storage, submit your job with --gres=spindle.

See the Compute node local drives page for further details and script examples.

ramfs - fast and highly temporary storage

On login nodes only, $XDG_RUNTIME_DIR is a ramfs, which means that it looks like files but is stored only in memory. Because of this, it is extremely fast, but has no persistence whatsoever. Use it if you have to make small temporary files that don’t need to last long. Note that this is no different than just holding the data in memory, if you can hold in memory that’s better.

Quotas

All directories under /scratch (as well as /home) have quotas. Two quotas are set per-filesystem: disk space and files number.

Disk quota and current usage are printed with the command quota. ‘space’ is for the disk space and ‘files’ for the total files number limit. There is a separate quota for groups on which the user is a member.

$ quota
User quotas for darstr1
     Filesystem   space   quota   limit   grace   files   quota   limit   grace
/home              484M    977M   1075M           10264       0       0
/scratch          3237G    200G    210G       -    158M      1M      1M       -

Group quotas
Filesystem   group                  space   quota   limit   grace   files   quota   limit   grace
/scratch     domain users            132G     10M     10M       -    310M    5000    5000       -
/scratch     some-group              534G    524G    524G       -    7534   1000M   1000M       -
/scratch     other-group              16T     20T     20T       -   1088M      5M      5M       -

If you get a quota error, see the quotas page for the solution.

Accessing and transferring files remotely

Transferring files to/from triton is exactly the same as any other remote Linux server.

Remote mounting using SMB

By far, remote mounting of files is the easiest method to transfer files. If you are not on the Aalto networks (wired, eduroam, or aalto with Aalto-managed laptop), connect to the Aalto VPN first. Note that this is automatically done on some department workstations (see below) - if not, request it!

The scratch filesystem can be remote mounted using SMB inside secure Aalto networks at the URLs

  • scratch: smb://data.triton.aalto.fi/scratch/.
  • work: smb://data.triton.aalto.fi/work/$username/.

On different operating systems:

  • Linux (Ubuntu for example): File manager (Nautilus) → File → Connect to server. Use the smb:// URLs above.
  • Windows: In the file manager, go to Computer (in menu bar on top, at least in Windows 10) → Map Network Drive) and “Map Network Drive”. In Windows 10 → “This PC” → right click → “Add Network Location”. (Note that this is different from right-click “Add network location” which just makes a folder link and has had some problems in the past.) Use the URLs above but replace smb:// with \\ and / with \. For example, \\data.triton.aalto.fi\scratch\.
  • Mac: Finder → Go → Connect to Server. Use the smb:// URLs above.

Depending on your OS, you may need to use either your username directly or AALTO\username.

Using sftp

The sftp protocol uses ssh to transfer files. On Linux and Mac, the sftp command line program are the must fundamental way to do this, and are available everywhere.

A more user-friendly way of doing this (with a nice GUI) is the Filezilla program.

Below is an example of the “raw” sftp usage:

# Copying from HOME to local PC
[email protected] $ sftp [email protected]:filename
Connected to triton.aalto.fi.
Fetching /home/user12/filename to filename
# copying to HOME
[email protected] $ sftp -b - [email protected] <<< 'put testCluster.m'
sftp> put foo
# copying to WRKDIR
[email protected] $ sftp -b - [email protected]:/scratch/work/USERNAME/ <<< 'put testCluster.m'
...

With all modern OS it is also possible to just open your OS file manager (e.g. Nautilus on Linux) and just put as address in the bar:

sftp://triton.aalto.fi

If you are connecting from remote and cannot use the VPN, you might connect instead to department machines like kosh, taltta, amor.

Rsync

Rsync is similar to sftp, but is smarter at restarting files. Use rsync for large file transfers. rsync actually uses ssh, so you can rsync from anywhere you can ssh from. Rsync is installed by default on Linux and Mac terminals. On Windows machines we recommend using GIT-bash.

While there are better places on the internet to read about rsync, it is good to try it out to sychronise a local folder on your triton’s scratch. Sometimes the issue with copying files is related to group permissions. This command takes care of permissions and makes sure that all your local files are identical (= same MD5 fingerprint) to your remote files:

rsync -avzc -e "ssh" --chmod=g+s,g+rw --group=GROUPNAME PATHTOLOCALFOLDER USERNAME@triton.aalto.fi:/scratch/DEPT/PROJECTNAME/REMOTEFOLDER/

Replease the bits in CAPS with your own case. Briefly, -a tries to preserve all attributes of the file, -v verbose to see what rsync is doing, -z use compression, -c skip files that have identical MD5 checksum, -e specify to use ssh (not necessary but needed for the commands coming after), –chmod sets the group permissions to shared (as common practice on scratch project folders), –group set the groupname to the group you belong to (note that GROUPNAME == PROJECTNAME on our scratch filesystem).

If you might want to just check if your local files are different from the remote ones. You can then run rsync in “dry run” so that you only see what the command would do, without actually doing anything.:

rsync --dry-run -avzc ...

Sometimes you want to copy only certain files. E.g. go through all folders, consider only files ending with py:

rsync -avzc --include '*/' --include '*.py' --exclude '*' ...

Sometimes you want to copy only files under a certain size (e.g. 100MB):

rsync -avzc --max-size=100m ...

Rsync does NOT delete files by default, i.e. if you delete a file from the local folder, the remote file will not be deleted automatically, unless you specify the --delete option.

Please note that when working with files containing code or simple text, git is a better option to synchronise your local folder with your remote one, because not only it will keep the two folders in sycn, but you will also gain version controlling so that you can revert to previous version of your code, or txt/csv files.

Remote mounting using sshfs

sshfs is a neat program that lets you mount remote filesystems via ssh only. It is well-supported in Linux, and somewhat on other operating systems. It’s true advantage is that you can mount any remote ssh server - it doesn’t have to be set up specially for SMB or any other type of mounting. On Ubuntu, you can mount by “File → Connect to server” and using sftp://triton.aalto.fi/scratch/work/USERNAME.

The below uses command line programs to do the same, and makes the triton_work on your local computer access all files in /scratch/work/USERNAME. Can be done with other folders.:

mkdir triton_work
sshfs USERNAME@triton.aalto.fi:/scratch/work/USERNAME triton_work

Note that ssh binds together many ways of accessing Triton, with a similar syntax and options. ssh is a very important program and binds together all types of remote access, and learning to use it well will help you for a long time.

Exercises

  1. Mount your work directory by SMB and transfer a file to Triton. Note that you must be on eduroam, the aalto with Aalto laptop, or connected to the Aalto VPN.
  2. Or, use rsync, sftp, or sshfs to transfer a file.
  3. (Advanced) If you have a Linux on Mac computer, study the rsync manual page and try to transfer a file.

Accessing files from Department workstations

This varies per department, with some strategies that work from everywhere.

These mounts that are already on workstations require a valid Kerberos ticket (usually generated when you log in). On long sessions these might expire, and you have to renew them with kinit to keep going.

Generic

The staff shell server taltta.aalto.fi has scratch and work mounted at /m/triton, and department directories are also in the standard paths /m/{cs,nbe}/{scratch,work}/.

NBE

Work directories are available at /m/nbe/work and group scratch directories at /m/nbe/scratch/$project/.

PHYS

Directories available on demand through SSHFS. See the Data transferring page at PHYS Intranet (accessible by PHYS users only).

CS

Work directories are available at /m/cs/work/, and group scratch directories at /m/cs/scratch/$project/.

Exercises

strace is a command which tracks system calls, basically the number of times the operating system has to do something. It can be used as a rudimentary way to see how much I/O load there is.

  1. Use strace -c to compare the number of system calls in ls, ls -l, ls --no-color, and ls --color. You can use the directory /scratch/scip/lustre_2017/many-files/ as a place with many files in it. How many system calls per file were there for each option?
  2. Using strace -c, compare the times of find and lfs find on the directory mentioned above. Why is it different?
  3. (Advanced, requires slurm knowledge from future tutorials) You will find some sample files in /scratch/scip/examples/io. Create a temporary directory and…
    1. Run create_iodata.sh to make some data files in data/
    2. Compare the IO operations of find and lfs find on this directory.
    3. use the iotest.sh script to do some basic analysis. How long does it take? Submit it as a slurm batch job.
    4. Modify the iotest.sh script to copy the data/ directory to local storage, do the operations, then remove the data. Compare to previous strategy.
    5. Use tar to compress the data while it is on lustre. Unpack this tar archive to local storage, do the operations, then remove. Compare to previous strategies.

Next steps

The next tutorial is about interactive jobs.

If you are doing anything IO heavy, you might want to read the advanced storage page.

Optimizing data storage isn’t very glamorous, but it’s an important part of high-performance computing. You can find some hints on the profiling page.

We have these related pages: