SoftRM

Abstract

Many-core systems are envisioned to leverage the ever-increasing demand for more powerful computing systems. To provide the necessary computing power, the number of Processing Elements integrated on-chip increases and NoC based infrastructures are adopted to address the interconnection scalability. The advent of these new architectures surfaces the need for more sophisticated, distributed resource management paradigms, which in addition to the extreme integration scaling, make the new systems more prone to errors manifested both at hardware and software. In this work, we highlight the need for Run-Time Resource management to be enhanced with fault tolerance features and propose SoftRM, a resource management framework which can dynamically adapt to permanent failures in a self-organized, workload-aware manner. Self-organization allows the resource management agents to recover from a failure in a coordinated way by electing a new agent to replace the failed one, while workload awareness optimizes this choice according to the status of each core. We evaluate the proposed framework on Intel Single-chip Cloud Computer (SCC), a NoC based many-core system and customize it to achieve minimum interference on the resource allocation process. We showcase that its workload-aware features manage to utilize free resources in more that 90% of the conducted experiments. Comparison with relevant state-of-the-art fault tolerant frameworks shows decrease of up to 67% in the imposed overhead on application execution.