Abstract
Current real-time embedded systems development frameworks lack support for the verification of properties using explicit time where counting time (i.e., durations) may play an important role in the development process. Focusing on the real-time constraints inherent to these systems, we present a framework that addresses the specification of duration properties for runtime verification by employing a fragment of metric temporal logic with durations. We also provide an overview of the framework, the synthesis tools, and the library to support monitoring properties for real-time systems developed in C++11. The results obtained provide clear evidence of the feasibility and advantages of employing a duration-sensitive formalism to increase the dependability of avionic controller systems such as the PX4 and the Ardupilot flight stacks.

Abstract
The use of parallel heterogeneous embedded architectures is needed to implement the level of performance required in advanced safety-critical systems. Hence, there is a demand for using high level parallel programming models capable of efficiently exploiting the performance opportunities. In this paper, we evaluate the incorporation of OpenMP, a parallel programming model used in HPC, into Ada, a language spread in safety-critical domains. We demonstrate that the execution model of OpenMP is compatible with the recently proposed Ada tasklet model, meant to exploit fine-grain structured parallelism. Moreover, we show the compatibility of the OpenMP and tasklet models, enabling the use of OpenMP directives in Ada to further exploit unstructured parallelism and heterogeneous computation. Finally, we state the safety properties of OpenMP and analyze the interoperability between the OpenMP and Ada runtimes. Overall, we conclude that OpenMP can be effectively incorporated into Ada without jeopardizing its safety properties.

Abstract
This chapter provides an overview of the book theme, motivating the need for high-performance and time-predictable embedded computing. It describes the challenges introduced by the need for time-predictability on the one hand, and high-performance on the other, discussing on a high level how these contradictory requirements can be simultaneously supported.

Abstract
Nowadays, the prevalence of computing systems in our lives is so ubiquitous that we live in a cyber-physical world dominated by computer systems, from pacemakers to cars and airplanes. These systems demand for more computational performance to process large amounts of data from multiple data sources with guaranteed processing times. Actuating outside of the required timing bounds may cause the failure of the system, being vital for systems like planes, cars, business monitoring, e-trading, etc. High-Performance and Time-Predictable Embedded Computing presents recent advances in software architecture and tools to support such complex systems, enabling the design of embedded computing devices which are able to deliver high-performance whilst guaranteeing the application required timing bounds. Technical topics discussed in the book include: Parallel embedded platforms, Programming models, Mapping and scheduling of parallel computations, Timing and schedulability analysis, Runtimes and operating systems. The work reflected in this book was done in the scope of the European project P SOCRATES, funded under the FP7 framework program of the European Commission. High-performance and time-predictable embedded computing is ideal for personnel in computer/communication/embedded industries as well as academic staff and master/research students in computer science, embedded systems, cyber-physical systems and internet-of-things.

Abstract

Abstract
This chapter focuses on the analysis of the timing behavior of software applications that expose real-time (RT) requirements. The state-of-the-art methodologies to timing analysis of software programs are generally split into four categories, referred to as static, measurement-based, hybrid, and probabilistic analysis techniques. First, we present an overview of each of these methodologies and discuss their advantages and disadvantages. Next, we explain the choices made by our proposed methodology in Section 5.2 and present the details of the solution in Section 5.3. Finally, we conclude the chapter in Section 5.4 with a summary.