Energy-Aware Task Mapping for Multi-Core Processor: A Machine Learning Based Approach

When parallel workloads do not scale with the core, it is challenging to utilize parallelism effectively, especially on distributed cache/memory multi-core platforms. The mapping of processes to cores affects both the cache/memory latencies and the power consumption, determining the energy efficiency of computing subsystems. Consequently, performance and energy metrics usually cannot be simultaneously optimal, which demands a trade-off optimization mapping strategy. To this end, this paper presents a machine learning-based Energy-aware task Mapping Predictor (EMP). EMP adjusts memory-intensive and compute-intensive programs mapping on a single computing node by capturing hardware resources and program characteristics. For parallel programs on multiple nodes, EMP tries to add nodes and adjusts the mapping strategy to improve program performance while controlling energy consumption. We evaluate EMP on both ARM and x86 platforms. In single-node experiments, EMP reduces the Energy Delay Product (EDP) by 68.35% and 60.45% on ARM (A) and ARM (B), respectively, and in multi-node experiments, EMP reduces the EDP by 29.52% as compared to clustered task mapping.