Quartile and Outlier Detection on Heterogeneous Clusters Using Distributed Radix Sort

In the past few years, performance improvements in CPUs and memory technologies have outpaced those of storage systems. When extrapolated to the exascale, this trend places strict limits on the amount of data that can be written to disk for full analysis, resulting in an increased reliance on characterizing in-memory data. Many of these characterizations are simple, but require sorted data. This paper explores an example of this type of characterization -- the identification of quartiles and statistical outliers -- and presents a performance analysis of a distributed heterogeneous radix sort as well as an assessment of current architectural bottlenecks.