Efficient on-chip resource management is crucial for Chip Multiprocessors (CMP) to achieve high resource utilization and enforce system-level performance objectives. Existing multiple resource management schemes either focus on intra-core resources or intercore resources, missing the opportunity for exploiting the interaction between these two level resources. Moreover, these resource management schemes either rely on trial runs or complex on-line machine learning model to search for the appropriate resource allocation, which makes resource management inefficient and expensive. To address these limitations, this paper presents a predictive yet cost effective mechanism for multiple resource management in CMP. It uses a set of hardware-efficient online profilers and an analytical performance model to predict the application’s performance with different intra-core and/or inter-core resource allocations. Based on the predicted performance, the resource allocator identifies and enfo...