11 posts tagged with "storage"

During August’s maintenance, we refreshed the operating system images for both login and compute nodes, upgraded Slurm to version 25.5.2, and upgraded Klone's filesystem (GPFS) for increased stability. We also introduced a new Globus OneDrive connector, making it easier than ever to transfer files between OneDrive and Hyak Klone or Kopah Storage.

Stay informed by subscribing to our mailing list and the UW-IT Research Computing Events Calendar. The next maintenance is scheduled for Tuesday, September 9, 2025 (the second Tuesday of the month).

Notable Updates​

• Node image updates – Routine updates plus installation of new Slurm utilities that we will test for job efficiency monitoring.
• Slurm upgrade to 25.5.2 – Resolves a bugs the --gres flag allowed resources to be allocated to more than one job in version 25.05.0 and fixes X11 forwarding. Read more about this version.
• GPFS upgrade to 5.1.9.11 – Improves stability and includes several bug fixes. Read more about this version.

New Features​

Globus OneDrive Connector – UW-IT Research Computing has added OneDrive as a connector to Globus, making transfers between OneDrive and Hyak Klone or OneDrive and Kopah Storage easier than ever before!

Things to note​

• Did you know that the UW Community are eligible for 5TB of storage on OneDrive as part of the Office 365 Suite? Click here to learn more.
• While OneDrive is HIPAA and FERPA compatible, encryption is not enforced for Globus transfers on any of our connectors (OneDrive, Kopah, Klone). As a reminder, Klone and Kopah are NOT aligned with HIPPA, please keep this in mind now that OneDrive can transfer to either cluster.
• Sharing with external partners is not enabled for our OneDrive or Klone connectors via Globus. Sharing is permitted for Kopah.
• Read more
  • Globus for data transfer with Hyak Klone
  • Globus for data transfer with Kopah Storage

Office Hours​

• Wednesdays at 2pm on Zoom. Attendees need only register once and can attend any of the occurrences with the Zoom link that will arrive via email. Click here to Register for Zoom Office Hours.
• Thursdays at 2pm in person in eScience. (address: WRF Data Science Studio, UW Physics/Astronomy Tower, 6th Floor, 3910 15th Ave NE, Seattle, WA 98195).
• See our office hours schedule, subscribe to event updates, and bookmark our UW-IT Research Computing Events Calendar.

If you would like to request 1 on 1 help, please send an email to help@uw.edu with "Hyak Office Hour" in the subject line to coordinate a meeting.

UW Community Opportunities​

• The Data Science and AI Accelerator pairs eScience Institute data scientists with researchers from any field of study to work on focused, collaborative projects. Collaborations may center on analysis of an existing dataset to answer a specific research question, an implementation of software for processing or analyzing data, data visualization tools, or tools for data interpretation. Accelerator Projects may be submitted at any time. Projects for Fall 2025 must be received by August 14th, 2025. LEARN MORE HERE.
• Applications for the CSDE Data Science and Demography Training program are due Friday, August 22nd by 5pm. An information session will take place Wednesday, August 13th at 10:00 a.m. DETAILS HERE.
• Cloud Clinic August 14 10-11am - guest presenter Niris Okram from AWS presenting on “The Utility of Capacity Blocks: Optimizing computing horsepower per budget dollar.” This will be followed by a short presentation on building small-scale (“Littlest”) JupyterHubs. LEARN MORE HERE.
• DubHacks - October 18 - October 19, 2025 - DubHacks 2025 takes you back to where it all began—the childhood bedroom. A space for imagination, curiosity, and bold ideas. Now, with code instead of crayons, you get to build what makes your younger self proud. No limits, just pure creativity. What will you create when you let yourself play?

Training Opportunities​

• Automating Research with Globus: The Modern Research IT Platform - Aug. 18, 2025, 9 a.m. – 12 p.m. (Pacific Time) This workshop introduces Globus Flows and its role in automating research workflows. Participants will explore data portals, science gateways, and commons, enabling seamless data discovery and access. Enroll here.
• CU-RMACC Webinars: Should I be Scared of AI? Aug. 18, 2025 - 3:00 PM - 4:00 PM EDT Throughout history, new technologies have sparked both excitement and fear—AI is no different. In this talk, Dr. Shelley Knuth, Assistant Vice Chancellor for Research Computing at the University of Colorado explores the common fears surrounding artificial intelligence, why we feel them, and how we can shift our perspective to focus on positive outcomes. We’ll look at practical ways to address risks, embrace innovation, and move forward with AI as a powerful tool rather than something to fear. Learn more and register.
• COMPLECS: Batch Computing (Part II): Getting Started with Batch Job Scheduling 08/21/25 - 2:00 PM - 3:30 PM EDT Learn more and register.
• NUG Community Call: A Birds-Eye View of Using Cuda with C/C++ on Perlmutter (Part 2) August 27, 2025, 11 a.m. - 12:30 p.m. PDT - In this two-part training series, users will be introduced to the basics of using CUDA on Perlmutter at NERSC. The training will focus on the basics of the Perlmutter architecture and NVIDIA GPUs, programming concepts with CUDA using C/C++. Event 2 focuses on advance kernel and custom cuda kernels in C/C++. Learn more and register.
• COMPLECS: Linux Tools for Text Processing 09/04/25 - 2:00 PM - 3:30 PM EDT Learn more and register.
• Python for HPC 09/09/25 - 2:00 PM - 3:30 PM EDT Learn more and register.
• COMPLECS: Data Transfer 09/18/25 - 2:00 PM - 3:30 PM EDT Learn more and register.
• COMPLECS: Interactive Computing 10/09/25 - 2:00 PM - 3:30 PM EDT Learn more and register.
• COMPLECS: Linux Shell Scripting 10/23/25 - 2:00 PM - 3:30 PM EDT Learn more and register.
• COMPLECS: Using Regular Expressions with Linux Tools 11/06/25 - 2:00 PM - 3:30 PM EST Learn more and register.
• COMPLECS: Batch Computing (Part III) High-Throughput and Many-Task Computing - Slurm Edition 12/04/25 - 2:00 PM - 3:30 PM EST Learn more and register.
• R for HPC 12/04/25 - 2:00 PM - 3:30 PM EST Learn more and register.

Positions​

• Two PhD positions in Artificial Intelligence - in collaboration with German Aerospace Center and TU Dresden, Germany. Deadline to apply: 27 August 2025. Apply Now!

Questions about Hyak Klone, Tillicum, or any other UW-IT Research Computing Service? Fill out our Research Computing Consulting intake form. We are here to help!

Happy Computing,

Hyak Team

If you haven't heard, we recently launched an on-campus S3-compatible object storage service called Kopah docs that is available to the research community at the University of Washington. Kopah is built on top of Ceph and is designed to be a low-cost, high-performance storage solution for data-intensive research.

While the deployment of Kopah was welcome news to those who are comfortable working with S3-compatible cloud solutions, we recognize some folks may be hesitant to give up their familiarity with POSIX file systems. If that sounds like you, we explored the use of JuiceFS, a distributed file system that provides a POSIX interface on top of object storage, as a potential solution.

Installation​

JuiceFS isn't installed by default so you will need to compile it yourself or download the pre-compiled binary from their release page.

As of January 2025 the latest version is 1.2.3 and you want the amd64 version if using from Klone. The command below will download and extract the binary to your current working directory.

wget https://github.com/juicedata/juicefs/releases/download/v1.2.3/juicefs-1.2.3-linux-amd64.tar.gz -O - | tar xzvf -

I have to move it to a folder in my $PATH so I can run it from anywhere by just calling the binary. Your personal environment varies here.

mv -v juicefs ~/bin/

Verify you can run JuiceFS.

npho@klone-login03:~ $ juicefs --version
juicefs version 1.2.3+2025-01-22.4f2aba8
npho@klone-login03:~ $ 

Cool, now we can start using JuiceFS!

Standalone Mode​

There are two ways to run JuiceFS, standalone or distributed mode. This blog post explores the former. Standalone mode is meant to only present Kopah via POSIX on Klone. The key points being:

1. There is an active juicefs process required to run while you want to access it.
2. It is intended for you to run it only on the node you are running the process from.

If you wanted to run JuiceFS on multiple nodes or with multiple users then we will have another proof-of-concept with distributed mode in the future.

Create Filesystem​

JuiceFS separates the data (placed into S3 object storage) and the metadata, which is kept locally in a database. The command below will create the myjfs filesystem and store the metadata in a SQLite database called myjfs.db in the directory where the command is run. It puts the data itself into a Kopah bucket called npho-project.

juicefs format \
  --storage s3 \
  --bucket https://s3.kopah.uw.edu/npho-project \
  --access-key REDACTED \
  --secret-key REDACTED \
  sqlite3://myjfs.db myjfs

You can rename the metadata file and the filesystem name to whatever you want (they don't have to match). The same goes for the bucket name on Kopah. However, I would strongly recommend having unique metadata file names that match the file system names for ease of tracking alongside the bucket name itself.

npho@klone-login03:~ $ juicefs format \
>   --storage s3 \
>   --bucket https://s3.kopah.uw.edu/npho-project \
>   --access-key REDACTED \
>   --secret-key REDACTED \
>   sqlite3://myjfs.db myjfs
2025/01/31 11:52:47.940709 juicefs[1668088] <INFO>: Meta address: sqlite3://myjfs.db [interface.go:504]
2025/01/31 11:52:47.944930 juicefs[1668088] <INFO>: Data use s3://npho-project/myjfs/ [format.go:484]
2025/01/31 11:52:48.666657 juicefs[1668088] <INFO>: Volume is formatted as {
  "Name": "myjfs",
  "UUID": "eb47ec30-c1f7-4a92-9b17-23c4beae7f76",
  "Storage": "s3",
  "Bucket": "https://s3.kopah.uw.edu/npho-project",
  "AccessKey": "removed",
  "SecretKey": "removed",
  "BlockSize": 4096,
  "Compression": "none",
  "EncryptAlgo": "aes256gcm-rsa",
  "KeyEncrypted": true,
  "TrashDays": 1,
  "MetaVersion": 1,
  "MinClientVersion": "1.1.0-A",
  "DirStats": true,
  "EnableACL": false
} [format.go:521]
npho@klone-login03:~ $ 

You can verify there is now a myjfs.db file in your current working directory. It's a SQLite database file that will store your file system meta data.

We can also verify the npho-project bucket was created on Kopah to store the data itself.

npho@klone-login03:~ $ s3cmd -c ~/.s3cfg-default ls                                      
2025-01-31 19:48  s3://npho-project
npho@klone-login03:~ $ 

You should run juicefs format --help to view the full range of options and customize the parameters of your file system to your unique needs but just briefly:

• Encryption: When you create the file system and format it you can see it has encryption by default using AES256. You can over ride this using the --encrypt-algo flag if you prefer chacha20-rsa or you can use key file based encryption and provide your private key using the --encrypt-rsa-key flag.
• Compression: This is not enabled by default and there is a computational penalty for doing so if you want to access your files since it needs to be de or re encrypted on the fly.
• Quota: By default there is no block (set with --capacity in GiB units) or inode (set with --inodes files) quota enforced at the file system level. If you do not explicitly set this, it will be matched to whatever you get from Kopah. This is still useful for setting explicitly if you wanted to have multiple projects or file systems in JuiceFS that use the same Kopah account and have some level of separation.
• Trash: By default, files are not deleted immediately but moved to a trash folder similar to most desktop systems. This is set with the --trash-days flag and you can set it to 0 if you want files to be deleted immediately. The default here is 1 day after which the file is permanently deleted.

Mount Filesystem​

Running the command below will mount your newly created file system to the myproject folder in your home directory. It does not need to previously exist.

juicefs mount sqlite3://myjfs.db ~/myproject --background

This process occurs in the background.

npho@klone-login03:~ $ juicefs mount sqlite3://myjfs.db ~/myproject --background
2025/01/31 11:57:01.652279 juicefs[1690855] <INFO>: Meta address: sqlite3://myjfs.db [interface.go:504]
2025/01/31 11:57:01.654920 juicefs[1690855] <INFO>: Data use s3://npho-project/myjfs/ [mount.go:629]
2025/01/31 11:57:02.156898 juicefs[1690855] <INFO>: OK, myjfs is ready at /mmfs1/home/npho/myproject [mount_unix.go:200]
npho@klone-login03:~ $ 

Use Filesystem​

Now with the file system mounted (at ~/myproject) you can use it like any other POSIX file system.

npho@klone-login03:~ $ cp -v LICENSE myproject 
'LICENSE' -> 'myproject/LICENSE'
npho@klone-login03:~ $ ls myproject 
LICENSE
npho@klone-login03:~ $ 

Remember, you won't be able to see it in the bucket because it is encrypted before being stored there.

Recover Deleted Files​

If you enabled the trash can option then you can recover files up until the permanent delete date.

First delete a file on the file system.

npho@klone-login03:~ $ cd myproject 
npho@klone-login03:myproject $ rm -v LICENSE 
removed 'LICENSE'
npho@klone-login03:myproject $

Verify the file is deleted. Go to recover it from the trash bin.

npho@klone-login03:myproject $ ls          
npho@klone-login03:myproject $ ls -alh          
total 23K
drwxrwxrwx  2 root root 4.0K Jan 31 12:54 .
drwx------ 48 npho all  8.0K Jan 31 13:08 ..
-r--------  1 npho all     0 Jan 31 11:57 .accesslog
-r--------  1 npho all  2.6K Jan 31 11:57 .config
-r--r--r--  1 npho all     0 Jan 31 11:57 .stats
dr-xr-xr-x  2 root root    0 Jan 31 11:57 .trash
npho@klone-login03:myproject $ ls .trash  
2025-01-31-20
npho@klone-login03:myproject $ ls .trash/2025-01-31-20 
1-2-LICENSE
npho@klone-login03:myproject $ cp -v .trash/2025-01-31-20/1-2-LICENSE LICENSE
'.trash/2025-01-31-20/1-2-LICENSE' -> 'LICENSE'
npho@klone-login03:myproject $ ls                     
LICENSE
npho@klone-login03:myproject $ 

As you can see, we can recover files that are tracked by their delete date. You would need to copy the file back out to recover it.

Unmount Filesystem​

When you are done using the file system you can unmount it with the command below.

npho@klone-login03:~ $ juicefs umount myproject
npho@klone-login03:~ $ 

Remember, the file system is only accessible in standalone mode so long as a juicefs process is running. Since we ran it in the background you will need to explicitly unmount it.

Questions?​

Hopefully you found this proof-of-concept useful. If you have any questions for us, please reach out to the team by emailing help@uw.edu with Hyak somewhere in the subject or body. Thanks!

Thanks again for your patience with our monthly scheduled maintenance. During this maintenance session, we were able to provide package updates to node images to ensure compliance with the latest operating system level security fixes and performance optimizations.

The next maintenance will be Tuesday September 10, 2024.

New self-hosted S3 storage option: KOPAH​

We are happy to announce the preview launch of our self-hosted S3 storage called KOPAH. S3 storage is a solution for securely storing and managing large amounts of data, whether for personal use or research computing. It works like an online storage locker where you store can files of any size, accessible from anywhere with an internet connection. For researchers and those involved in data-driven studies, it provides a reliable and scalable platform to store, access, and analyze large datasets, supporting high-performance computing tasks and complex workflows.

S3 uses buckets as containers to store data, where each bucket can hold 100,000,000 objects, which are the actual files or data you store. Each object within a bucket is identified by a unique key, making it easy to organize and retrieve your data efficiently. Public links can be generated for KOPAH objects so that users can share buckets and objects with collaborators.

Click here to learn more about KOPAH S3.

Who should use KOPAH?​

KOPAH is a storage solution for anyone. Just like other storage options out there, you can upload, download, and view your storage bucket with specialized tools and share your data via the internet. For Hyak users, KOPAH provides another storage option for research computing. It is more affordable than /gscratch storage and can be used for active research computing with a few added steps for retreiving stored data prior to a job.

Test Users Wanted​

Prior to September, we are inviting test users to try KOPAH and provide feedback about their experience. If you are interested in becoming a KOPAH test user, please each help@uw.edu with Hyak or KOPAH in the subject line.

Requirements:

1. While we will not charge for the service until September 1, to sign up as a test user, we require a budget number and worktag. If the service doesn't work for you, you can cancel before September.
2. We will ask for a name for the account. If your groups has an existing account on Hyak, klone /gscratch, it makes sense of the names to match across services.
3. Please be ready to respond with your feedback about the service.

Opportunities​

PhD students should check out this opportunity for funding from NVIDIA: Graduate Research Fellowship Program

Questions? If you have any questions for us, please reach out to the team by emailing help@uw.edu with Hyak in the subject line.

Thank you for your patience this month while there was more scheduled downtime than usual to allow for electrical reconfiguration work in the UW Tower data center. We appreciate how disruptive this work has been in recent weeks. Please keep in mind that this work by the data center team has been critical in allowing the facility to increase available power to the cluster to provide future growth capacity, which was limiting deployment of new equipment in recent months.

The Hyak team was able to use the interruption to implement the following changes:

• Increase in checkpoint (--partition=ckpt) runtime for GPU jobs from 4-5 hours to 8-9 hours (pre-emption for requeuing will still occur subject to cluster utilization). Please see the updated documentation page for information about using idle resources.
• The NVIDIA driver has been updated for all GPUs.

Our next scheduled maintenance will be Tuesday May 14, 2024.

Training Opportunities​

Follow NSF ACCESS Training and Events posting HERE to find online webinars about containers, parallel computing, using GPUs, and more from HPC providers around the USA.

Questions? If you have any questions for us, please reach out to the team by emailing help@uw.edu with Hyak in the subject line.

It has come to our attention that the default configuration of Miniconda and conda environments in the user's home directory leads to hitting storage limitations and the dreaded error Disk quota exceeded. We thought we would take some time to guide users in configuring their conda environment directories and package caches to avoid this error and proceed with their research computing.

Conda's config​

Software is usually accompanied by a configuration file (aka "config file") or a text file used to store configuration data for software applications. It typically contains parameters and settings that dictate how the software behaves and interacts it's environment. Familiarity with config files allows for efficient troubleshooting, optimization, and adaptation of software to specific environments, like Hyak's shared HPC environment, enhancing overall usability and performance. Conda's config file .condarc, is customizable and lets you determine where packages and environments are stored by conda.

Understanding your Conda​

First let's take a look at your conda settings. The conda info command provides information about the current conda installation and its configuration.

conda info

The output shoudl look something like this if you have installed Miniconda3.

     active environment : None
            shell level : 0
       user config file : /mmfs1/home/UWNetID/.condarc
 populated config files : /mmfs1/home/UWNetID/.condarc

          conda version : 4.14.0
    conda-build version : not installed
         python version : 3.9.5.final.0
       virtual packages : __linux=4.18.0=0
                          __glibc=2.28=0
                          __unix=0=0
                          __archspec=1=x86_64
       base environment : /mmfs1/home/UWNetID/miniconda3  (writable)
      conda av data dir : /mmfs1/home/UWNetID/miniconda3/etc/conda
  conda av metadata url : None
           channel URLs : https://conda.anaconda.org/conda-forge/linux-64
                          . . .
          package cache : /mmfs1/home/UWNetID/conda_pkgs
       envs directories : /mmfs1/home/UWNetID/miniconda3/envs
               platform : linux-64
             user-agent : conda/4.14.0 requests/2.26.0 CPython/3.9.5 Linux/4.18.0-513.18.1.el8_9.x86_64 rocky/8.9 glibc/2.28
                UID:GID : 1209843:226269
             netrc file : None
           offline mode : False

The paths shown above will show your username in place of UWNetID. Notice the highlighted lines above showing the absolute path to your config file in your home directory (e.g., /mmfs1/home/UWNetID/.condarc), the directory designated for your package cache (e.g., /mmfs1/home/UWNetID/conda_pkgs), and the directory/ies designated for your environments (e.g., /mmfs1/home/UWNetID/miniconda3/envs). Conda designates directories for your package cache and your environments by default, but under Hyak, your home directory has a 10G storage limit, which can quickly be maxed out by package tarballs and their contents. We can change the location for your package cache and your environments to avoid this.

ls -a

Configuring your package cache and envs directories​

If you don't have a .condarc in your home directory, you can create and edit it with a hyak preloaded editor like nano or vim. Here we will used nano.

nano ~/.condarc

Edit OR ADD the highlighted lines to your .condarc to designate directories with higher storage quotas for our envs_dirs and pkgs_dirs. In this exercise, we will assign our envs_dirs and pkgs_dirs directories to directories in /gscratch/scrubbed/ where we have more storage, although remember scrubbed storage is temporary and files are deleted automatically after 21 days if the timestamps are not updated. Alternatively, your lab/research group might have another directory in /gscratch/ that can be used.

Here is what your edited .condarc should look like.

envs_dirs:
  - /gscratch/scrubbed/UWNetID/envs
pkgs_dirs:
  - /gscratch/scrubbed/UWNetID/conda_pkgs
always_copy: true

In addition to designating the directories, please include always_copy: true, which is required on the Hyak filesystem for configuring your conda in this way.

After .condarc is edited, we can use conda info with grep to see if our changes have been incorporated.

conda info |grep cache 

The result should be something like

/gscratch/scrubbed/UWNetID/conda_pkgs

And for the environments directory

conda info |grep envs

Result

/gscratch/scrubbed/UWNetID/envs

Cleaning up disk storage​

After you have reset the package cache and environment directories with your conda config file, you can delete the previous directories to free up storage. Before doing that, you can monitor how much storage was being occupied by each item in your home directory with the command du -h --max-depth=1. Remove directories previously used as cache and envs_dir recursively with rm -r. The following is an example of monitoring storage and removing directories.

Below is an example output from the du -h --max-depth 1 command

du -h --max-depth=1 /mmfs1/home/UWNetID/
6.7G	./miniconda3/
4.0G	./conda_pkgs
. . .
rm -r /mmfs1/home/UWNetID/miniconda3/envs
du -h --max-depth=1 /mmfs1/home/UWNetID/
2.6G	./miniconda3/
4.0G	./conda_pkgs
. . .

Storage can also be managed by cleaning up package cache periodically. Get rid of the large-storage tar archives after your conda packages have been installed with conda clean --all.

Lastly, regular maintenance of conda environments is crucial for keeping disk usage in check. Review you list of conda environments with conda env list and remove unused environments using the conda remove --name ENV_NAME --all command. Consider creating lightweight environments by installing only necessary packages to conserve disk space. For example, create an environment for each project (project1_env) rather than an environment for all projects combined (myenv).

Disk quota STILL exceeded​

Be aware that many software packages are configured similarly to conda. Explore the documentation of your software to locate the configuration file and anticipate where storage limitations might become an issue. In some cases, you may need to edit or create a config file for the software to use. pip and R are two other common offenders ballooning the disk storage in your home directory.

Configuring PIP​

If you are installing with pip, you might have a pip cache in ~/.cache/pip. Let's locate your the pip config file location under variant "global." You might have to activate a previously built conda environment to do this. For this exercise we will use an environment called project1_env.

conda activate project1_env
(project1_env) $ pip config list -v
. . .
For variant 'user', will try loading '/mmfs1/home/UWNetID/.pip/pip.conf'
. . .

The message "will try loading" rather than listing the config file pip.conf means that a pip config file has not been created. We will create our config file and set our pip cache. Create a directory in your home directory (e.g.,/mmfs1/home/UWNetID/.pip) to hold your pip config file and create a file called pip.conf with the touch command. Remember to also create the new directory for your new pip cache if you haven't yet.

mkdir /mmfs1/home/UWNetID/.pip/
touch /mmfs1/home/UWNetID/.pip/pip.conf
mkdir /gscratch/scrubbed/UWNetID/pip_cache

Open pip.conf with nano or vim and add the following lines to designate the location of your pip cache.

[global]
cache-dir=/gscratch/scrubbed/UWNetID/pip_cache

Check that your pip cache has been designated.

(project1_env) $ pip config list
/mmfs1/home/UWNetID/.pip/pip.conf
(project1_env) $ pip cache dir
/gscratch/scrubbed/UWNetID/pip_cache

Configuring R​

We previously covered this in our documentation. Edit or create a config file called .Renviron in your home directory. Use nano or vim to designate the location of your R package libraries. The contents of the file should be something like the following example.

R_LIBS="/gscratch/scrubbed/UWNetID/R/"

The directory designated by R_LIBS will be where R installs your package libraries.

I'm still stuck​

Please reach out to us by emailing help@uw.edu with "hyak" in the subject line to open a help ticket.

For our March maintenance we had some notable changes we wanted to share with the community.

Login Node​

Over the last several months the login node has been crashing on occasion. We have been monitoring and dissecting the kernel dumps from each crash and this behavior seems to be highly correlated with VS Code Remote-SSH extension activity. To prevent node instability, we have upgraded the storage drivers to the latest version. If you are a VS Code user and connect to klone via Remote-SSH, we have some recommendations to help limit the possibility that your work would cause system instability on the login node.

Responsible Usage of VS Code Extension Remote-SSH​

While developing your code with connectivity to the server is a great usage of our services, connecting directly to the login node via the Remote-SSH extension will result in VS Code server processes running silently in the background and leading to node instability. As a reminder, we prohibit users running processes on the login node.

1. Check which processes are running on the login node, especially if you have been receiving klone usage violations when you are not aware of jobs running. Look for vscode-server among the listed processes.

   $ ps aux | grep UWNetID
   

2. If you need to develop your code with connectivity to VS Code, use a ProxyJump to open a connection directly to a compute node. Step 1 documentation. and then use the Remote-SSH extension to connect to that node through VS Code on your local machine, preserving the login node for the rest of the community. Step 2 documentation.

3. Lastly, VS Code’s high usage is due to it silently installing its built in features into the user's home directory ~/.vscode on klone enabling intelligent autocomplete features. This is a well known issue, and there is a solution that involves disabling the @builtin TypeScript plugin from the VS Code on your local machine. Here is a link to a blog post about the issue and the super-easy solution. Disabling @builtin TypeScript will reduce your usage of the shared resources and avoid problems.

In addition to the upgrade of the storage driver, we performed updates to security packages.

Training Opportunities​

We wanted to make you aware of two training opportunities with the San Diego Supercomputer Center. If you are interested in picking up some additional skills and experience in HPC, check this blog post.

Questions?​

If you have any questions for us, please reach out to the team by emailing help@uw.edu with Hyak in the subject line.

Hello Hyak community! We have a few notable announcements regarding this month’s maintenance. If the hyak-users mailing list e-mail didn’t fully satisfy your curiosity, hopefully this expanded version will answer any lingering questions.

GPUs​

• Software: The GPU driver was upgraded to the latest stable version (545.29.06). The latest CUDA 12.3.2 is also now provided as a module. You are also encouraged to explore the use of container (i.e., Apptainer) based workflows, which bundle various versions of CUDA with your software of interest (e.g., PyTorch) over at NGC. NOTE: Be sure to pass the --nv flag to Apptainer when working with GPUs.

• Hardware: The Hyak team has also begun the early deployments of our first Genoa-Ada GPU nodes. These are cutting-edge NVIDIA L40-based GPUs (code named “Ada”) running on the latest AMD processors (code named “Genoa”) with 64 GPUs released to their groups two weeks ago and an additional 16 GPUs to be released later this week. These new resources are not currently part of the checkpoint partition but we will be releasing guidance on making use of idle resources here over the coming weeks directly to the Hyak user documentation as we receive feedback from these initial researchers.

Storage​

• Performance Upgrade: In recent weeks, AI/ML workloads have been increasingly stressing the primary storage on klone (i.e., "gscratch"). Part of this was attributed to the run up to the International Conference for Machine Learning (ICML) 2024 full paper deadline on Friday, February 2. However, it also reflects a broader trend in the increasing demands of data-intensive research. The IO profile was so heavy at times that our systems automation throttled the checkpoint capacity to near 0 in order to keep storage performance up and prioritize general cluster navigation and contributed resources. We have an internal tool called iopsaver that automatically reduces IOPS by intelligently requeuing checkpoint jobs generating the highest IOPS while concurrently limiting the number of total active checkpoint jobs until the overall storage is within its operating capacity. At times over the past few weeks you may have noticed that iopsaver had reduced the checkpoint job capacity to near 0 to maintain overall storage usability.

  During today’s maintenance, we have upgraded the memory on existing storage servers so that we could enable Local Read-Only Cache (LROC) although we don’t anticipate it will be live until tomorrow. Once enabled, LROC allows the storage cluster to make use of a previously idle SSD capacity to cache frequently accessed files on this more performant storage tier medium. We expect LROC to make a big difference as during this period of the last several weeks, the majority of the recent IO bottlenecking was attributed to a high volume of read operations. As always, we will continue to monitor developments and adjust our policies and solutions accordingly to benefit the most researchers and users of Hyak.

• Scrubbed Policy: In the recent past this space has filled up. As a reminder, this is a free-for-all space and a communal resource for when you have data you only need to temporarily burst out into past your usual allocations from your other group affiliations. To ensure greater equity among its use, we have instituted a 10TB and 10M files limit for each user in scrubbed. This impacts <1% of users as only a handful of users were using an amount of quota from scrubbed >10TB.

Questions?​

Hopefully you found these extra details informative. If you have any questions for us, please reach out to the team by emailing help@uw.edu with Hyak somewhere in the subject or body. Thanks!

We’ve made an update to our storage accounting tool, hyakstorage, and with this update we are also phasing out usage_report.txt. That text file contained minimally-parsed internal metrics of the storage cluster, and we found it caused as many questions as it answered. Moving forward, the hyakstorage tool will display only the four relevant pieces of information for each fileset you query: storage space used vs. the storage space limit, and current amount of files (inodes) vs. maximum number of files.

The default operation–running hyakstorage with no arguments–will show your home directory & the gscratch directories you have access to, and it will only show the fileset totals & your contributions.

You can also specify which filesets you want to view, in a few different ways: you can use the flag --home to show your home directory, --gscratch to show your gscratch directories, and --contrib to show your group’s contrib directories. You can also specify an exact gscratch directory with the group name (e.g. hyakstorage stf), contrib directory (e.g. hyakstorage stf-src), or full path to a fileset (e.g. hyakstorage /mmfs1/gscratch/stf).

If you want more detailed metrics, you can use the flags --show-user or --show-group to break down the fileset totals by individual users or groups. Those detailed metrics can be sorted by space with --by-disk (the default) or by files with --by-files.

See also:

• gscratch documentation and
• hyakstorage documentation.

While the storage on klone (i.e., mmfs1 or gscratch) may appear to be a monolithic device, it is an extremely complex cluster in its own right. This storage cluster is mounted on every klone node: so despite appearing as "on the node", gscratch physically resides on specialized storage hardware separated from the compute resources of klone. The storage is accessed across a high-speed, ultra low-latency HDR Infiniband network, and is designed to be scalable independent of KLONE’s compute resources.

As mentioned in an earlier blog post today, our incoming hardware expansion will drastically increase the amount of demand the storage cluster can handle. In the meantime, the Hyak team has taken measures to help maintain a usable level of storage performance for users and jobs:

1. Improved internal storage metrics gathering and visibility.​

The Hyak team improved storage-cluster metric gathering and visibility, allowing us to correlate those metrics to reports of poor user experience, and to make data-driven tuning and storage policy decisions.

In the figure above we have visibility into if an abnormally high number of jobs have errors that might suggest underlying storage or other user experience issues.

2. Created custom filesystem migration policies to optimize the use of the NVMe layer.​

The bulk of the storage capacity on klone is stored on rotary hard disk drives totalling approximately 1.7 Petabytes (PB) of raw storage. In addition to the hard disk storage, there is a much smaller, extremely fast–and expensive–pool of NVMe "flash" storage that functions both as a write buffer for new files written to the filesystem, and also as a read-cache-like layer where files can be read without causing load on the rotary disks.

The Hyak team has also optimized the file placement policy: files most likely to generate heavy load reside in the limited space of the NVMe layer, ensuring that no storage load is generated on the hard disk layer when those files are repeatedly accessed.

In the figure above you can see that the flash tier (green line) is allowed to fill up to 80% capacity due to job writes then the migration policy begins until the flash tier is down to 65% full. For the majority of the past few several weeks we can see things worked as expected. However, there were a few events recently where jobs were producing so much data that the flash tier was able to get to 100% full faster than the storage system could move data off the flash tier. Giving the migration process too high of a priority results in "slowness" in the user experience. We have since been tuning the aggressiveness of this migration process to reduce the likelihood of it occuring again.

3. Added QoS policies to improve worst-case filesystem responsiveness.​

The klone filesystem has a coarse Quality-of-Service (QoS) tuning facility that allows the filesystem to cap the rate of storage operations for various types of storage input-output (IO). The Hyak team has used this facility in two different ways:

1. First, to limit the storage load impact when the NVMe layer, described above, needs to free up space by moving files to the hard drive layer.

2. Secondly, to moderate the amount of storage load that can be generated by any single compute node in the cluster. This way, outlier jobs in terms of storage load generation are less likely to have an outsized performance impact on the storage.

4. Manually identifying jobs causing a disproportionate impact on storage performance.​

Utilizing metrics and old-fashioned sleuthing, we have been manually tracking down individual jobs that appear to be having a disproportionate and/or unnecessary impact on storage performance, and working with users to address the storage performance impact of these jobs.

In the above figure we can see job IO follows a power law dynamic, a small handful of jobs are often responsible for the majority of load. In this case a single job on a single node is responsible. When users report storage "slowness" this disrepancy can be even more pronounced but we are able to quickly narrow down which specific nodes are responsible and address these corner cases.

5. Dynamically reducing the number of running checkpoint partition jobs.​

As of April 19th, 2022, we have implemented data-driven automation to moderate storage load by dynamically managing the number of running checkpoint (ckpt) partition jobs. When the number of running ckpt jobs is being limited, pending jobs will show AssocGrpJobsLimit as the REASON for not starting.

Please note that non-ckpt jobs (i.e., jobs submitted to nodes your lab contributed to the cluster) are not limited in any way. The social contract when joining the Hyak community is that you get access to the nodes your lab contributes on-demand, and–if and when they are idle–access to other labs’ resources on the cluster. However, access to other labs’ resources isn’t and hasn’t ever been guaranteed: it’s just that there’s often a steady state idle capacity for users to "burst" into by submitting ckpt jobs.

In aggregate, 'Storage Load' is a consumable resource just like CPU cores or memory, albeit one that impacts the whole cluster when it is over-consumed. The Slurm cluster scheduler cannot directly consider storage load availability when evaluating resources for starting ckpt jobs, hence our need to automate. Our new tooling limits the storage performance impact from ckpt jobs in order to improve storage stability for everyone.

The red and blue lines represent two storage servers that we have most closely tied to the user experience and 50% load being the threshold we aim to remain at or under by dynamically reducing the number of running ckpt jobs when it exceeds that limit.

So far, this appears to be very effective at moderating the overall storage load, preventing the storage cluster from becoming unusably slow and avoiding other storage-performance issues. We will continue to tune it in search of the best balance between idle resource utilization via ckpt and storage performance.

6. Expanding the team​

Acknowledging that the storage sub-system is a complicated machine in its own right, it needs much more care and attention and the current Hyak team is stretched incredibly thin as is. We have started the process of hiring a dedicated research data storage systems engineer to focus on optimizing storage going forward.

See also:

• A summary of the state of the union on klone storage.
• Things you as a researcher using klone can do to optimize your storage use.

While some of what precipitated this conversation is the current state of the storage (i.e., mmfs1 or gscratch), there are several things you can do as a researcher to both reduce the load on gscratch as well as help insulate your jobs from cluster-wide storage slowdowns.

1. Use local node SSDs.​

Each node on the cluster has a local SSD drive with 350+ GB of space available for use by user jobs. This space is available only to jobs running on that node and all contents are purged when the users’ last job running on the node completes. It is mounted as /scr and /tmp (both paths go to the same place) on all the compute nodes.

If input data, Apptainer (Singularity) images, or other files used by your job will fit, copying those files to the SSD (via cp, rsync, etc.) once at the beginning of your job and reading them from there during the remainder of the job run results in less load on the central storage, helps insulate your job from any instances of central storage slowness, and can often result in better overall job performance.

Slurm has a command called sbcast www that is useful for efficiently copying files to all nodes used in a multi-node job as part of an sbatch script.

For files being written that need to be kept after the job run, it is generally best to write these directly to the central storage. Because new files are written directly to the very fast NVMe layer, such writes are less likely to impact overall storage performance. That said, it is still beneficial to write intermediate job files to the local SSD whenever possible.

2. Code for efficient file IO.​

While this can be a very complicated topic, a great deal of overall job performance can be gained by thoughtful and judicious use of file input-output (IO). Some general tips:

• Keep in mind that file access is orders of magnitude slower than memory access, and processes often have to completely "stop and wait" for disk IO operations to complete. Minimizing file IO operations, especially inside "inner loops" of programs can greatly speed up job completion, and helps to reduce load on the cluster central storage.
• Fewer, larger file IO operations are generally more efficient than multiple smaller file operations accessing the same data.
• When possible, store data in an efficient format such as HDF5 instead of many small files.
• "Open/read once, access many times" if job memory permits.

3. Containerize your environment.​

As mentioned above, minimizing the number of files you need to access can help reduce the number of input / output operations per second (IOPS) happening on the cluster. For example, a Python miniconda environment can create hundreds or even thousands of small files when you install different library dependencies. While Python is a common compute environment, this can be generalized to most other programs you may need. When you containerize your environment, this gets reduced to a single file. A brief introduction to Singularity (now called Apptainer) can be found here. As a side benefit, containerizing your environment–making it a single file–makes it much easier to move it around (see #1 above).

4. Stay under quota.​

Constantly hitting your inode (e.g., file) or block (e.g., number of GBs or TBs) quotas can cause extra storage slowness. If you need a bump on either please reach out to discuss your options. As a reminder you can us the hyakstorage command on klone to display current quota usage for all of your filesets as well as your home directory. Please note that this output is updated once an hour so it will take time to reflect any overages.

5. Report issues.​

While the Hyak team has an extensive monitoring and alerting framework in place to help us to proactively determine when things may be going wrong, not all causes of slow user experience are currently correlated to metrics. Furthermore, our team generally interfaces with the cluster in different ways than our users, so we may not be as equally exposed to any pains until it is reported to us. If you’ve run into a performance issue, please submit a ticket by emailing help@uw.edu. Please provide any symptoms you are observing, along with the date, timeframe, job IDs (if applicable), commands you are running with their full output, etc. If you don’t need or want a reply from us it is still helpful for us to hear from you, feel free to say "no response needed" or something along these lines so we know how to respond.

See also:

• A summary of the state of the union on klone storage.
• Things the Hyak team has done (and currently doing) to optimize the storage environment.