  Writing data in parallel is a common operation in some computing environments
and a good proxy for a number of other parallel processing patterns. The
duration of time taken to write data in large-scale compute environments can
vary considerably. This variation comes from a number of sources, both
systematic and transient. The result is a highly complex behavior that is
difficult to characterize. This paper further develops the model for parallel
task variability proposed in the paper "A parallel workload has extreme
variability" (Henwood et. al 2016). This model is the Generalized Extreme Value
(GEV) distribution. This paper further develops the systematic analysis that
leads to the GEV model with the addition of a traffic congestion term.
Observations of a parallel workload are presented from a High Performance
Computing environment under typical production conditions, which include
traffic congestion. An analysis of the workload is performed and shows the
variability tends towards GEV as the order of parallelism is increased. The
results are presented in the context of Amdahl's law and the predictive
properties of a GEV models are discussed. A optimization for certain machine
designs is also suggested.
