In self-adapting embedded real-time systems, operating systems and software provide mechanisms to self-adapt to changing requirements. Autonomous adaptation decisions introduce novel risks as they may lead to unforeseen system behavior that could not have been specified within a design-time model. However, as part of its functionality the operating system has to ensure the reliability of the entire self-x system during run-time.In this paper, we present our work in progress for an operating system framework which aims to identify anomalous or malicious system states at run-time without a sophisticated specificationtime model. Inspired by the Artificial Immune Systems Danger Theory, we propose an anomaly detection mechanism that operates not only on the local system behavior information of the monitored component. Furthermore, to ensure an efficient behavior evaluation, the anomaly detection mechanism implies system-wide input signals that indicate e.g the existence of a potential danger within the overall system or the occurrence of a system adaption. Due to the ability of this framework to cope with dynamically changing behavior and to identify unintended behavioral deviations, it seems to be a promising approach to enhance the run-time dependability of a self-x system.

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Hardware accelerators are becoming popular in academia and industry. To move one step further from the state-of-the-art multicore plus accelerator approaches, we present in this paper our innovative SAVEHSA architecture. It comprises of a heterogeneous hardware platform with three different high-end accelerators attached over PCIe (GPGPU, FPGA and Intel MIC). Such systems can process parallel workloads very efficiently whilst being more energy efficient than regular CPU systems. To leverage the heterogeneity, the workload has to be distributed among the computing units in a way that each unit is well-suited for the assigned task and executable code must be available. To tackle this problem we present two software components; the first can perform resource allocation at runtime while respecting system and application goals (in terms of throughput, energy, latency, etc.) and the second is able to analyze an application and generate executable code for an accelerator at runtime. We demonstrate the first proof-of-concept implementation of our framework on the heterogeneous platform, discuss different runtime policies and measure the introduced overheads

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Multi-accelerator platforms combine CPUs and different accelerator architectures within a single compute node. Such systems are capable of processing parallel workloads very efficiently while being more energy efficient than regular systems consisting of CPUs only. However, the architectures of such systems are diverse, forcing developers to port applications to each accelerator using different programming languages, models, tools, and compilers. Developers not only require domain-specific knowledge but also need to understand the low-level accelerator details, leading to an increase in the design effort and costs.To tackle this challenge, we propose a compilation approach and a practical realization called HTrOP that is completely transparent to the user. HTrOP is able to automatically analyze a sequential CPU application, detect computational hotspots, and generate parallel OpenCL host and kernel code. The potential of HTrOP is demonstrated by offloading hotspots to different OpenCL-enabled resources (currently the CPU, the generalpurpose GPU, and the manycore Intel Xeon Phi) for a broad set of benchmark applications. We present an in-depth evaluation of our approach in terms of performance gains and energy savings, taking into account all static and dynamic overheads. We are able to achieve speedups and energy savings of up to two orders of magnitude, if an application has sufficient computational intensity, when compared to a natively compiled application.This article is an extension of a conference paper/poster at PPoPP'18 [49]. Prior work only outlined the tool flow and the parallelization mechanism. Evaluation is entirely new, including energy measurements and trends over several data points per application.