Lessons learned at 208k: towards debugging millions of cores

Petascale systems will present several new challenges to performance and correctness tools. Such machines may contain millions of cores, requiring that tools use scalable data structures and analysis algorithms to collect and to process application data. In addition, at such scales, each tool itself will become a large parallel application – already, debugging the full BlueGene/L (BG/L) installation at the Lawrence Livermore National Laboratory requires employing 1664 tool daemons. To scale to such counts and beyond, tools must employ a scalable communication infrastructure and manage their own tool processes efficiently. Some system resources, such as the file system, may also become a tool bottleneck. In this paper, we present challenges to petascale tool development, using the Stack Trace Analysis Tool (STAT) as a case study. STAT is a lightweight tool that gathers and merges stack traces from a parallel application to identify process equivalence classes. We use results gathered at thousands of tasks on an Infiniband cluster and results up to 208K processes on BG/L to identify current scalability issues as well as challenges that will be faced at the petas-cale. We then present solutions to these challenges that have been implemented and show the resulting performance improvements. We also discuss future plans to meet the debugging demands of petascale machines.