  Common pitfalls in visualization projects include lack of data availability
and the domain users' needs and focus changing too rapidly for the design
process to complete. While it is often prudent to avoid such projects, we argue
it can be beneficial to engage them in some cases as the visualization process
can help refine data collection, solving a "chicken and egg" problem of having
the data and tools to analyze it. We found this to be the case in the domain of
task parallel computing where such data and tooling is an open area of
research. Despite these hurdles, we conducted a design study. Through a
tightly-coupled iterative design process, we built Atria, a multi-view
execution graph visualization to support performance analysis. Atria simplifies
the initial representation of the execution graph by aggregating nodes as
related to their line of code. We deployed Atria on multiple platforms, some
requiring design alteration. We describe how we adapted the design study
methodology to the "moving target" of both the data and the domain experts'
concerns and how this movement kept both the visualization and programming
project healthy. We reflect on our process and discuss what factors allow the
project to be successful in the presence of changing data and user needs.
