Abstract The purpose of an application performance analysis tool is to help the user identify whether or not their application is running efficiently on the computing resources available. However, the scale of current and future high end systems, as well as increasing system software and architecture complexity, brings a new set of challenges to todays performance tools. In order to achieve high performance on these peta-scale computing systems, users need a new infrastructure for performance analysis that can handle the challenges associated with multiple levels of parallelism, hundreds of thousands of computing elements, and novel programming paradigms that result in the collection of massive sets of performance data. In this paper we present the Cray Performance Analysis Toolset, which is set on an evolutionary path to address the application performance analysis challenges associated with these massive computing systems by highlighting relevant data and by bringing Cray optimization knowledge to a wider set of users
