'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Modern heterogeneous multi-core systems, containing various types of cores, are increasingly dealing with concurrent execution of dynamic application workloads. Moreover, the performance constraints of each application vary, and applications enter/exit the system at any time. Existing approaches are not efficient in such dynamic scenarios, especially if applications are unknown, as they require extensive offline application analysis and do not consider the runtime execution scenarios (application arrival/completion, and workload and performance variations) for runtime management. To address this, we present AdaMD, an adaptive mapping and dynamic voltage and frequency scaling (DVFS) approach for improving energy consumption and performance. The key feature of the proposed approach is the elimination of dependency on offline profiled results while making runtime decisions. This is achieved through a performance prediction model having a maximum error of 7.9% lower than the previously reported model and a mapping approach that allocates processing cores to applications while respecting performance constraints. Furthermore, AdaMD adapts to runtime execution scenarios efficiently by monitoring the application status, and performance/workload variations to adjust the previous DVFS settings and thread-to-core mappings. The proposed approach is experimentally validated on the Odroid-XU3, with various combinations of diverse multi-threaded applications from PARSEC and SPLASH benchmarks. Results show energy savings of up to 28% compared to the recently proposed approach while meeting performance constraints
