Decentralized agent based re-clustering for task mapping of tera-scale network-on-chip system

With the rapid increasing demand for high-performance computing, such as cloud computing, Tera (flops) scale high-performance computing system composed of hundreds of on-chip processing cores has become the recent interest. Given a large-scale computing system such as network-on-chip (NoC) with hundreds of cores, bandwidth and power density are the fundamental limits dominated by on-chip communication. This has brought extreme challenge when mapping application tasks onto Tera-scale NoC system. Previous task mapping scheme is mainly centralized and static, and hence results in large communication volume, not scalable for runtime task mapping required by Tera-scale NoC system. In order to improve on-chip traffic and reduce power density for the need of Tera-scale NoC system, we have proposed a de-centralized re-clustering algorithm. The processing cores in the NoC system are organized into clusters with an efficient decentralized re-clustering scheme to adjust the cluster size for the task mapping. As such, the communication volume can be significantly reduced and result in decreased power. Experimental results have demonstrated that our proposed algorithm can achieve reduction of communication traffic (up to 66.7%). The energy consumption profile has also been efficiently improved to reduce the hotspots.