Towards a Parallel Benchmark for Space Applications: Distributing OBPMark’s Image Processing

Modern space applications require high computing power and high reliability from on-board processors. To meet these requirements, the German Aerospace Center (DLR) is developing a Scalable On-board Computer for Space Avionics (ScOSA) system with a distributed non-shard memory architecture. As performance is an important criterion in the selection of hardware for space missions, the European Space Agency has published an open source benchmark suite called OBPMark. It is a set of benchmarks based on typical space applications and designed to measure system-level performance. However, there is currently no standard tool for evaluating the performance of distributed on-board computers. In this paper, we propose a parallelization strategy for running the OBPMark image processing benchmark on a distributed on-board computer. We used a split-map-reduce model to integrate the #1.1 image calibration and correction benchmark of OBPMark into the ScOSA system. We evaluated the developed distributed benchmark on the existing ScOSA High Performance Nodes (HPNs) consisting of 5 Xilinx Zynq 7020 SoCs. The results show a significant reduction of the benchmark execution time from 9.0 to 2.8 seconds using 5 nodes. In the case of dual-core with 4 nodes, the execution time was reduced to 2.5 seconds. We conclude that OBPMark is a valuable tool for evaluating the performance of distributed on-board computers with non-shared memory architectures and contributes to the standardisation of performance evaluation in the space domain.