  High Performance Compute (HPC) clusters often produce intermediate files as
part of code execution and message passing is not always possible to supply
data to these cluster jobs. In these cases, I/O goes back to central
distributed storage to allow cross node data sharing. These systems are often
high performance and characterised by their high cost per TB and sensitivity to
workload type such as being tuned to small or large file I/O. However, compute
nodes often have large amounts of RAM, so when dealing with intermediate files
where longevity or reliability of the system is not as important, local RAM
disks can be used to obtain performance benefits. In this paper we show how
this problem was tackled by creating a RAM block that could interact with the
object storage system Ceph, as well as creating a deployment tool to deploy
Ceph on HPC infrastructure effectively. This work resulted in a system that was
more performant than the central high performance distributed storage system
used at Diamond reducing I/O overhead and processing time for Savu, a
tomography data processing application, by 81.04% and 8.32% respectively.
