Dark silicon is a phenomenon of under-utilization in today's manycore systems due to power and thermal limitations. In order to improve the performance of dark silicon systems, it is necessary to adopt dynamic power constraints for different core mapping decisions. However, existing power budgeting methods are generally over pessimistic, e.g., Thermal Safe Power (TSP), or over optimistic, e.g., Greedy based Dynamic Power (GDP). This paper proposes a practical power budgeting method, called Combinational Optimization Power (COP). Different from existing methods, which ignore some actual factors, such as communication overhead and lifetime reliability, COP formulates the power budgeting problem as a thermal-constrained combinational optimization power problem. For the steady-state case, COP achieves the target fusion of optimized temperature and communication energy consumption by applying task priority ranking and task-to-core mapping. For the transient case, COP uses the rainflow counting algorithm to construct the reliability framework based on the thermal cycling failure mechanism, and then establishes a linear time-invariant transient temperature model to obtain the core mapping selection and the corresponding dynamic power budget. Experimental results demonstrate that COP is capable of providing an optimized core mapping decision, which can maximize power budget while ensuring the system performance.

In order to improve the performance of many-core systems, it is necessary to adopt dynamic power constraints for different core mapping decisions. However, existing power budgeting methods are generally over pessimistic, e.g. Thermal Safe Power (TSP), or over optimistic, e.g. Greedy based Dynamic Power (GDP). This paper proposes a practical power budgeting method, called Combinational Optimization Power (COP). Different from existing methods, which ignore some actual factors, such as communication overhead and lifetime reliability, COP formulates the power budgeting problem as a thermal-constrained combinational optimization power problem. For the steady-state case, COP achieves the target fusion of optimized temperature and communication energy consumption by applying task priority ranking and task-to-core mapping. For the transient case, COP uses the rainflow counting algorithm to construct the reliability framework based on the thermal cycling failure mechanism, and then establishes a linear time-invariant transient thermal model to obtain the core mapping selection and the corresponding dynamic power budget. Experimental results show that COP is capable of providing an optimized core mapping decision, which can maximize power budget while ensuring the system performance.