  This paper documents the experience improving the performance of a data
processing workflow for analysis of the Human Connectome Project's HCP900 data
set. It describes how network and compute bottlenecks were discovered and
resolved during the course of a science engagement. A series of computational
enhancements to the stock FSL BedpostX workflow are described. These
enhancements migrated the workflow from a slow serial execution of computations
resulting from Slurm scheduler incompatibilities to eventual execution on GPU
resources, going from a 21-day execution on a single CPU core to a 2 hour
execution on a GPU. This workflow contributed a vital use-case to the build-out
of the campus compute cluster with additional GPUs and resulted in enhancements
to network bandwidth. It also shares insights on potential improvements to
distribution of scientific software to avoid stagnation in site-specific
deployment decisions. The discussion highlights the advantages of open licenses
and popular code collaboration sites like GitHub.com in feeding contributions
upstream.
