Worst-Case Execution-Time-Aware Parallelization of Model-Based Avionics Applications

Abstract

Multi-core processing systems are the solution of choice to provide high embedded computing performance, but drawbacks in timing-predictability and programmability limit their adoption in safety-critical aerospace applications. This work presents a compiler tool-flow for automated parallelization of model-based real-time software, which addresses the shortcomings of multi-core architectures in real-time systems. The flow is demonstrated using a model-based Terrain Awareness and Warning Systems (TAWS) and an edge detection algorithm from the image-processing domain. Model-based applications are first transformed into real-time C code and from there into a well-predictable parallel C program. Tight bounds

CLUTCH: A Clustering-Driven Runtime Estimation Scheme for Scientific Simulations

“…Table 1 summarizes major similarities and differences between our work and the existing studies. First, the examined papers concern various application domains: load sharing facility (LSF) [8], parallel program [9], [11], [25], cloud [10], [29], HPC [2], [14], [15], [19], location-based services [20]- [23], databases [26]- [28], big data applications [29], [30], and scientific workloads [12], [13], [16]- [19]. The runtime estimation problem addressed in this paper applies to the scientific workloads domain.…”

“…Estimating the worst-case execution time is also discussed in some other works [24], [25]. Besides, there are some works [27], [28] concerning query time estimation in the database context.…”

“…Some of these [26], [27], [29] developed (pure) analytical models and assess the validity of the model. Many of the existing studies [20]- [24], [28] build neural-net based learning models and utilize them for deriving estimated time; many others [2], [10], [16]- [19], [25], [30] use tree and linear regression based machine learning models. Some other works [11], [12], [14], [15] use hybrid methods combining these tools-analytical model, machine learning, and deep learning.…”