File systems and I/O¶
The main filesystem is 19 Petabytes of diskspace in
/fred. This filesystem has more than 30 GB/s of aggregate bandwidth. It is a Lustre + ZFS filesystem with high performance, reliability, and redundancy.
There are a few other smaller filesystems in the cluster. Of these,
/home is cluster-wide like
$JOBFS is on SSDs in each compute node.
/home is a Lustre + ZFS filesystem and
$JOBFS is XFS.
Each compute node has a local SSD (fast disk) that is accessible from batch jobs. If the I/O patterns in your workflow are inefficient on the usual cluster filesystems, then you should consider using these local disks.
$JOBFS environment variable in each job points at a per-job directory on the local SSD. Space on local disks is requested from
slurm with eg.
#SBATCH --tmp=20GB. The
$JOBFS directory is automatically created before each job starts, and deleted after the job ends.
A typical workflow that uses local disks would be to copy tar files from
$JOBFS, untar, do processing on many small files using many IOPS, tar up the output, copy results back to
On OzSTAR, the two main cluster-wide directories are:
/fred is vastly bigger and faster in (almost) every way than
/home, but it is NOT backed up.
/fred is meant for large data files and most jobs should be reading and writing to it.
/fred is not backed up, it is unsuitable for long term data storage. Data loss due to hardware failure is unlikely, but possible. Accidentally deleted files cannot be recovered.
The user is responsible for backing up any data that is important to an external location.
/home is backed up.
/home is intended for small source files and important scripts and input.
Typically project leaders will create directories for each of their members inside
/fred/<project_id>/<username>). If a user is a member of multiple projects then they will have access to multiple areas in
Disk quotas are enabled on OzSTAR.
/home has a per-user limit of 10GB blocks and 100,000 files. This cannot be increased. These numbers are set so that backups are feasible.
/fred has a default per-project limit of 10TB blocks and 1M files. If you require additional storage, please contact firstname.lastname@example.org
Because of filesystem compression (see below), it is common to store more data than this and remain under quota. This is because quotas count only actual blocks used on disk.
To check your quota on any node, type:
/home filesystems have transparent compression turned on. This means that all files (regardless of type) are internally compressed by the filesystem before being stored on disk, and are automatically uncompressed by the filesystem as they are read.
This means that you will not save diskspace if you gzip your files, because they are already compressed.
For example, a highly compressible file like a typical ASCII input file:
% ls -ls input 1 -rw-r--r-- 1 root root 39894 Mar 5 17:36 input
the file is ~40KB in size, but it uses less than 1KB on disk (the first column).
For a typical binary output file that is somewhat compressible:
% ls -lsh output 7.8M -rw-rw-r-- 1 root root 33M Feb 20 18:25 output
so 7.8MB of space on disk is used to store this 33MB file.
This means that although the nominal capacity of
/fred is ~5 PiB (output from
df is somewhat pessimistic), the amount of data that can be stored is greater. How much greater depends upon the compressibility of typical data.
If you are transferring files over the network to other machines then it would still make sense to have your files compressed (gzip, bzip, xz etc.) in order to minimise both network bandwidth and diskspace used at the other end. But internally to the supercomputer there is no need to compress data. Doing so is fine, but unnecessary - it will just result in the data being slightly larger because it is compressed twice by slightly different algorithms.
Hints for Optimising Lustre I/O¶
The best way to get good performance to any filesystem (not just Lustre) is to do reads and writes in large chunks and to use large files rather than many small files. Performing many IOPS (input/output operations per second) should be avoided.
What is “large”?¶
For Lustre, reads and writes of >4MB are ideal. 10 MB and above is best. Small (eg. < 100 kB) reads and writes and especially 4k random writes should be avoided. These cause seeking and obtain low I/O performance from the underlying raids and disks. Small sequential reads are often optimised by read-ahead in block devices or Lustre or ZFS so may perform acceptably, but it's unlikely.
The best way to get high I/O performance from large parallel codes (eg. a checkpoint) is generally to read or write one large O(GB) file per process, or if the number of processes is very large, then one large file per node. This will send I/O to all or many of the Object Storage Targets (OSTs) that make up the
/fred filesystem and can run at over 30 GB/s (not including speed-ups from transparent filesystem compression).
Each of the individual OSTs in the
/fred filesystem is composed of 4 large raidz3's in a ZFS pool and is capable of several GB/s. Because each OST is large and fast there is no advantage in using Lustre striping.
What are IOPS?¶
IOPS are Input/Output Operations per Second. I/O operations are things like open, close, seek, read, write, stat, etc.. IOPS is the rate at which these occur.
High IOPS and small files tend to go hand in hand. Optimal cluster file sizes are usually between 10 MB and 100 GB. Using anything smaller than 10 MB files risks having its I/O time dominated by open()/close() operations (IOPS), of which there are a limited amount available to the entire file system.
A pathologically bad file usage pattern would be a code that accesses 100,000 files in a row, each of <8k in size. This will perform extremely badly on anything except a local disk. It is not an efficient usage model for a large shared supercomputer file system (see the Local Disk section above). Similarly, writing a code that has open()/close() in a tight inner loop will be dominated by the metadata operations to the Lustre MDSs (MetaData Servers), will perform badly, and will also impact the use of the cluster for all users because the MDS is a shared resource and can only do a finite number of operations per second (approx 100k).
Other things to avoid¶
File lock bouncing is also an issue that can affect POSIX parallel file systems. This typically occurs when multiple nodes are appending to the same shared “log” file. By its very nature the order of the contents of such a file are undefined - it is really a “junk” file. However Lustre will valiantly attempt to interlace I/O from each appending node at the exact moment it writes, leading to a vast amount of “write lock bouncing” between all the appending nodes. This kills the performance of all the processes appending, and from the nodes doing the appending, and increases the load on the MDS greatly. Do not append to any shared files from multiple nodes.
In general a good rule of thumb is to not write at all to the same file from multiple nodes unless it is via a library like MPI IO.