HPC-Mesh: A Homogeneous Parallel Concentrated Mesh for Fault-Tolerance and Energy Savings
Proceedings of the 2011 ACM/IEEE Seventh Symposium on Architectures for Networking and Communications Systems
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We present a novel network on-chip topology, PC-Mesh (Parallel Concentrated Mesh), suitable for tiled CMP systems. The topology is built using four concentrated mesh (C-Mesh) networks and a new network interface able to inject packets through different networks. The goal of the new combined topology is to minimize the power consumption of the network when running applications exhibiting low traffic rates and maximize throughput when applications require high traffic rates. Thus, the topology is dynamically adjusted (switching on and off network components) with a proper injection algorithm, adapting itself to the network on-chip traffic requirements. The PC-Mesh network performs as a C-Mesh network (using one sub network) when the traffic is low obtaining large savings in power consumption. When the load network increases, new sub networks are opened and thus higher traffic rates are supported, thus providing comparable results as the mesh network. Additional benefits of the PC-Mesh network is its fault tolerance degree and the lower latency in terms of hops. An alternative PC-Mesh version is provided to optimize the fault-tolerance degree. Comparative results with detailed evaluations (in area, power, and delay) are provided both for the network interface and switches. Results demonstrate PC-Mesh is able to dynamically adapt to the current traffic situations. Experimental results with a system-level simulation platform (including the application being run and the operating system) are provided. Results show how the PC-Mesh network achieves the same results as the C-Mesh topology reducing execution time of applications by 20% as well as energy consumption by also 20%, when compared with the 2D-Mesh network topology. However, when challenged with higher traffic demands, PC-Mesh outperforms the C-Mesh network by achieving much lower execution time of applications and lower energy consumption. In some scenarios, execution time is reduced by a factor of 2 and power consumption by 50%.