Online Performance Analysis: An Event-based Workflow Design Towards Exascale

Today, it is commonly accepted that speedup and efficiency are not granted automatically when developing or porting software for High Performance Computing (HPC) platforms. The reasons are manifold and actively investigated in the community. Software monitors are essential for these studies as they provide the raw performance data to be analyzed. We propose an online monitoring workflow for event-based performance analysis that takes into account the significant changes in system architecture towards the development of exascale supercomputers. Critical properties are: communication across large numbers of processing elements, limited I/O capabilities, and a decreasing memory-per- core ratio. We present a hierarchical data management and steering workflow that directly couples the application, measurement, and analysis processes, thus eliminating the need for extensive communication and buffering. The workflow is closely integrated with the native system communication API to enable best communication across processing elements. The memory issue is addressed with a new lossy hierarchical data compression technique for in-memory storage, intended for small, fixed-size buffers. Further, we abandon secondary storage to avoid potential I/O challenges. We demonstrate the feasibility of our design with a prototype implementation that features services for data collection, analysis, and runtime compression. Our evaluation extrapolates results obtained with the NAS Parallel Benchmarks at up to 2,048 processes to an exascale workflow.