Understanding new JASMIN storage

(as of Phase 4: 2018/19)

JASMIN continues to grow as a unique collaborative analysis environment for an expanding community of scientists. Some of the big challenges we attempted to address with Phase 4 were the ever-growing demand for storage space and the increasing diversity of scientific workflows. However, we’re aware that some aspects of the changes introduced in Phase 4 have presented some challenges in themselves. Here, we outline the reasons for the changes and try to summarise some of the challenges and what can be done to help deal with them. 

Background (skip this if you just want to go straight to the advice):

With phase 4 we knew we had to both replace existing storage that had become uneconomic to maintain, and add significantly more volume! However, we also knew that most of the data stored on JASMIN disk is not touched for months on end, but that some data is heavily used. We also knew that the traditional way of building disk systems was no longer suitable for the scales (volumes of data) we needed to handle, being supplanted by new technologies, and that at some point our community would have to get used to these new technologies too.  The solution we chose for JASMIN is the same solution being deployed at most large HPC sites: deploying tiered storage, that is more types of storage, and requiring you, the user, to use the right kind of storage in the right place in your workflow!

Understanding the four types of JASMIN disk:

We have settled on four kinds of disk storage - quite an increase from the one we had previously! Each is best for one kind of workflow, although each “can do” most things, although not always well. We will see below that there is one kind of activity that we now need to be much more careful about, because doing it not only causes problems for individuals, but also for everyone else. We could stop allowing this to happen, but it would be at a performance penalty which would occur all the time: we have gone for “better with occasional really slow” in preference to “always predictably slow” performance. What we need you to do is learn how to avoid creating the “occasionally really slow” times!

The four types are:

  • Solid state fast but not parallel disk (SSD), really suitable for small files. This is what is used for your /home/users directories, so is good for things you really don’t want to lose, because this area is backed up. The same type of storage is also used for the scratch area /work/scratch-nopw2, although this is NOT for persistent storage and is NOT backed up. SSD is great for compiling and storing millions of small files, but is the most expensive storage, so we don’t have a lot of it.
  • Fast parallel disk (PFS), great for jobs that read and write the same file from many different processes. This is what we had before. It’s not so great for lots of small files. This is still pretty expensive, which is one of the reasons why we haven’t simply stayed with it. Some GWS still use this, but most are migrating to the next category - scale-out file storage.
  • Scale Out File Storage (SOF). This is what most of our Group Workspaces (GWS) will use. This is great for large volumes of data with regular use (consider near-line tape storage if you don’t need access for a significant period). SOF is not so great for small files, so if you have lots of small files, best to either aggregate them or tar them up. This is *terrible* for parallel *write* access to files, and you must avoid that. More details on that are below, but the key point is you might find you need to use the fast parallel scratch (currently /work/scratch-nopw2) in your workflows as an intermediary between persisting your data and your Lotus batch jobs.
  • High Performance Object Storage (HPOS). This is a new type of storage, and it’s best if you are working with the cloud. It’s going to be a bit tricky to get the hang of, so pay attention to the various things we’re going to be saying over the next few months about how to use it. It is the future though...

What type of storage am I using?

Storage Type Parallel-write Good for small files? Backed up?
/home/users SSD no yes e.g. Installing Conda yes
/group_workspaces/jasmin2 PFS yes no no
/gws/pw/j05 PFS yes no no
/gws/nopw/j04/ SOF no no no
/gws/smf/j04/ SSD no yes no
/work/scratch-pw [closing Nov 2022]
PFS yes no no
/work/scratch-nopw2 SSD no yes no
/work/xfc/volX SOF no no no

Automount SOF storage: Please note that the new storage of GWSs - /gws/nopw/j04/ is automounted. This means that a GWS is not mounted by a particular host until the moment they are first accessed. If the workspace you are expecting to see is not listed at the top level ( /gws/nopw/j04/) you should ls the full path of the workspace, and after a very short delay, the workspace should appear.

See also here to see where these are mounted throughout JASMIN.

The BIG issue: why you need to be careful about parallel file access (even if you don’t think you are doing it):

Traditional disk systems try and do cunning things when different processes are writing to the same file; they can lock the file so only one process can have a turn at a time, or they can try and stack up the updates and do them one after another (and hope they don’t interfere), but at scale, all those tricks come with a performance cost. That cost gets paid in many ways: raw I/O speed, how many extra copies of blocks get written, how long it takes to rebuild if things go wrong, how big any part of the storage can be…  and, how much kit and software the vendors need to deliver to make it work. All that cost is worth it if your workflow needs it (and can’t avoid it), but in the JASMIN environment, not many workflows actually need it.

Our fast parallel disk is fine for those workflows, but none of the others support it well, and in particular for our scale-out file storage, as used by most GWSs, the way it works means that if we turn on the support for parallel write, it will become much slower and write many more copies of some data blocks, meaning it will do I/O slower, and it will store less! Not what we want. The parallel read is fine! However, avoiding parallel writes has turned out to be harder than we anticipated, your workflows have many more ways of doing it than we thought!  Sadly, when you do parallel writes, the file system can get “stuck” and that’s when everything goes really slow, for everyone on that host …

One way around this is for us to apply “global write locking” to a GWS volume (your GWS manager would need to request this). This solves the problem by preventing parallel writes altogether, but at a significant cost in performance.

FAQs, issues and solutions

Please read our collection of FAQs and known issues (and solutions!) which we've put together HERE.

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