LO-FA-MO: Fault Detection and Systemic Awareness for the QUonG Computing System

Abstract

QUonG is a parallel computing platform developed at INFN and equipped with commodity multi-core CPUs coupled with last generation NVIDIA GPUs. Computing nodes communicate through a point-to-point, high performance, low latency 3D torus network implemented by the APEnet+ FPGA-based interconnect. Scaling of this cluster towards peta-and possibly exascale is a prominent investigation point and in this context fault tolerance issues are structural. Typical fault tolerance solutions for HPC systems (e.g. checkpoint/restart) need to be triggered to be applied in an automated and transparent way, or at least knowledge about occurring faults needs propagating in order to prompt a readjustment: an effective tool to detect faults and make the system aware of them is required. Thus, as a first step towards a fault tolerant QUonG we designed the Local Fault Monitor (LO|FA|MO), an HW/SW solution aimed at providing systemic fault awareness. LO|FA|MO allows the detection of node faults thanks to a mutual watchdog mechanism between the host and the APEnet+ NIC, moreover, diagnostic messages can be delivered to neighbour nodes through both the 3D network and a secondary connection for service communication. The double path ensures that no fault remains unknown at the global level, guaranteeing systemic fault awareness with no single point of failure. In this paper we describe our LO|FA|MO implementation, reporting preliminary measures that show scalability and its next to nil impact on system performance.