3 posts tagged with "spectrum scale"

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.

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.

The 3rd generation Hyak cluster, klone, launched in spring 2021 with 144 HPC nodes and 192 GPUs. In just a single year, we’ve grown to over 384 HPC nodes (a 166% increase) and 448 GPUs (a 133% increase). klone has more than doubled in size, and while some of this growth comes from long-standing Hyak members migrating to the new cluster, much of our increased capacity comes from hundreds of new researchers joining the Hyak community. We’ve seen existing sponsors such as the College of Engineering increase their already substantial footprints by 60%, we’ve welcomed new sponsors such as UW Bothell, UW Tacoma, and the Puget Sound Institute, and seen over 1000% growth–seriously–in our new self-sponsored tier for investigators and faculty without an existing Hyak sponsor affiliation. As with any large project, during KLONE’s initial planning stages we made assumptions about our growth rate & the types of research we would be supporting: assumptions that have been shattered by our growth over the past year. It was never a question of if we would need to upgrade our support infrastructure–like storage–but when, and our rapid growth significantly accelerated our upgrade timeline.

Monitoring – and developing more monitoring for – the Hyak clusters is a central responsibility of our team. The status quo at the beginning of 2022 was to track down errant jobs or workflows when storage issues came up. In almost every instance, we were able to pinpoint the problematic job and work with the researcher to shape their code into a normal IO profile. Pausing jobs and providing best practices was sufficient to keep the storage performance solid for everyone. However, starting around the last week of March 2022, we started having trouble finding an obvious job, or even a set of jobs, impacting storage performance.

The truth is that our baseline load had shifted. Due to our tremendous growth, things researchers had previously been doing without issue were now causing problems. We also noticed an evolution of the types of research happening on klone. The Hyak community diversified from traditional HPC workflows (e.g., simulations) into more data-intensive areas like data science (e.g., R jobs), deep learning, and artificial intelligence research. We accelerated our discussions with storage vendors: in a few short months, an expansion went from an eventuality to an immediate and pressing need. Still, we tried several last-minute optimizations to see if we could prevent spending all that money. We are serious about our fiduciary duty, as stewards of this research platform, to provide the most value for the Hyak community with the dollars we are entrusted with. We knew a storage upgrade for klone would cost hundreds of thousands of dollars and we needed absolute certainty that we couldn’t engineer a way around that expense.

The storage on klone (i.e., mmfs1 or gscratch) might pretend to be a mere folder or directory, but in truth it’s an abstraction of a highly complex system. To provide cost-effective, high-performance storage, a small high-speed NVMe "flash" layer acts both as a write buffer for the slower spinning disks–which make up the vast majority of cluster’s capacity–and as a high-speed "cache" for recently & frequently accessed small files. While presented as a single folder to the researcher, behind the scenes the storage cluster moves data between these tiers to balance performance. As seen in the figure above, when the flash layer reaches 80% capacity, a process begins to drain it by moving less frequently used files to the spinning-disk layer until the flash layer reaches 65% capacity. You might also notice that despite our precautions and monitoring, as of April 9, 2022, we were no longer able to migrate data from flash to spinning disks faster than our users were writing. This was the final deciding factor for us, and we initiated our long-standing plan to upgrade the storage for klone.

This necessary investment to upgrade storage will double both the maximum input-output operations-per-second (IOPS) and throughput (storage bandwidth), providing much needed overhead for current workflows as well as accommodating future growth. We are excited for this upgrade – and are doing everything we can to expedite its deployment – but due to the sheer amount of hardware we’re purchasing, we’ve been swept up in the pandemic-induced global supply chain crunch. Our vendors have predicted that the end of July is the worst-case scenario, but that a June delivery is also possible. We will update the Hyak community as we know more. As always, we welcome any questions: if you want to speak with us about something, send an email to the Hyak team via help@uw.edu and we’ll follow up with you.

See also:

• Things the Hyak team has done (and currently doing) to optimize the storage environment.
• Things you as a researcher using klone can do to optimize your storage use.