Feedback Computing in Leadership Compute Systems

Leadership class systems are heavily shared resource environments with users contending for shared system resources. This results in users experiencing huge performance variations, and also affects the overall throughput of the system. To alleviate the problem, system software tools must be built taking into consideration user requirements and resource availability, a feedback driven approach. Realizing a feedback-based compute environment for peta-scale systems have two challenging tasks. First, collecting discreet, coarse-grained system statistics from multiple systems using minimum system resources and without affecting the user jobs is a hard problem. Second, with discreet data collected from disparate sources the challenge is in associating the data for meaningful interpretations to drive feedback-based decision systems in real-time. In this paper, we elaborate on a feedback-based computing framework with respect to the peta-scale compute and storage system at the Oak Ridge Leadership Computing Facility. We describe our feedback-based approach for dynamic resource allocation, context-aware scheduling and application checkpointing.