Adaptive on-the-fly Application Performance Modeling for Many Cores

Resource management for a many-core system entails allocating cores to applications and binding tasks of the applications to particular cores. Accurate on-the-fly estimates of different core allocations w.r.t. application performance are required before binding the tasks to cores for execution efficiency. We propose an adaptive on-the-fly application performance model that largely alleviates this increasingly important problem. It allows reacting to spontaneous workload variations and it considers topological properties of resources. Extensive evaluations show that the average estimation error is reduced from 14.7% to 4.5%, resulting in high quality of on-the-fly adaptive application mappings. Our work is a first milestone towards optimality of systems that exhibit a high degree of spontaneous workload variations.