We present a new approach to analytical performance modeling using Aspen, a domain specific langauge. Aspen (Abstract Scalable Performance Engineering Notation) fills an important gap in existing performance modeling techniques and is designed to enable rapid exploration of new algorithms and architectures. It includes a formal specification of an application's performance behavior and an abstract machine model. We provide an overview of Aspen's features and demonstrate how it can be used to express a performance model for a three dimensional Fast Fourier Transform. We then demonstrate the composability and modularity of Aspen by importing and reusing the FFT model in a molecular dynamics model. We have also created a number of tools that allow scientists to balance application and system factors quickly and accurately.

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.

Non-expert users face a dilemma when making security decisions. Their security often cannot be fully automated for them, yet they generally lack both the motivation and technical knowledge to make informed security decisions on their own. To help users with this dilemma, we present a novel security user interface called Sesame. Sesame uses a concrete, spatial extension of the desktop metaphor to provide users with the security-related, visualized system-level information they need to make more informed decisions. It also provides users with actionable controls to affect a system's security state. Sesame graphically facilitates users' comprehension in making these decisions, and in doing so helps to lower the bar for motivating them to participate in the security of their system. In a controlled study, users with Sesame were found to make fewer errors than a control group which suggests that our novel security interface is a viable alternative approach to helping users with their dilemma.