Modern safety critical systems require high levels of performance for advanced functionalities, which are not possible with the simple conventional architectures currently used in them. Embedded General Purpose Graphics Processing Units (GPG-PUs) are among the hardware technologies which can provide the high performance required in these domains. However, their massively parallel nature complicates the verification of their software and increases its cost because it usually involves code coverage through extensive human-driven testing. The Ada SPARK language has traditionally been used in highly-critical environments for its formal verification capabilities and powerful type system. The use of such tools which are backed up by theorem provers, has significantly lowered the amount of effort needed to validate functionality of safety-critical systems. In this European Space Agency (ESA) funded project, we utilize AdaCore's CUDA backend for Ada in conjunction with the SPARK language subset to assess the state of static verification for GPU kernels. We assess the error detection capabilities of the available tools and we formulate a methodology to maximise their effectiveness. Moreover, our project results using ESA's open source GPU4S Benchmarking suite show that common programming mistakes in GPU software development can be prevented.

Considering the complexity in the design of future satellites and the need for compliance with ECSS standards, the METASAT project proposes a novel design methodology based on model-based engineering and supported by open architecture hardware. The initiative highlights the potential of software vir-tualisation layers, like hypervisors, to meet standards compliance on high-performance computing platforms. Our focus is on the development of a toolchain tailored for these advanced hard-ware/software layers. In our view, without such innovation, the satellite industry may face high and unsustainable development costs and timelines, which could affect its competitiveness and reliability. This paper provides an overview of the model-based engineering toolchain, the workflow, and the digital twin concept proposed for the METASAT project. We present the purpose of each component and how they work together in the broader METASAT vision, with the objective of showing how this approach can make the development process more efficient and enhance dependability in the sector.

Given the complex design requirements of future satellites and the need to comply with ECSS standards, the METASAT project introduces an innovative design approach that uses model-based engineering and is supported by open architecture hardware. The project highlights the importance of software virtualisation layers, such as hypervisors, in achieving compliance with standards on high-performance computing plat-forms. The project is dedicated to creating a specialised toolchain for these advanced hardware and software layers. We believe that without such innovations, the satellite industry could face increased and unsustainable development costs and timelines, potentially compromising its competitiveness and dependability. This paper provides a general overview of the METASAT project and introduces the approaches being used to add support for the XtratuM hypervisor and code generation for hardware accelerators in a model-based engineering workflow.

Introducing advanced functionalities in safety-critical systems requires using more powerful architectures such as GPUs. However software in safety-critical industries is subject to functional certification, which cannot be achieved using standard GPU programming languages such as CUDA and OpenCL. Fortunately, GPUs are already used in certified critical systems for display tasks, using safety-certified solutions such as OpenGL SC 2.0. In this paper, we compare two state-of-the art graphics-based methodologies, OpenGL SC 2.0 and Brook Auto/BRASIL for the implementation of a prototype avionics case study. We evaluate both methods on a realistic industrial setup, composed by an avionics-grade GPU and a safety-certified GPU driver in terms of development metrics and performance, showing their feasibility.

Embedded General Purpose Graphics Processing Units (GPGPUs) are increasingly used in automotive to enable Advanced Driving Assistance (ADAS) and Autonomous driving. However, their functional safety certification has been addressed so far only at the application or hardware level. In this work, we demonstrate how the entire GPGPU software stack can be developed and certified up to ASIL-D, including its toolchain. We introduce BRASIL, a GPGPU toolchain targeting high-integrity GPGPU computing and we explain our methodology to achieve ISO 26262 compliance, as well as our tool qualification strategy.

Modern automotive systems require increased performance to implement Advanced Driving Assistance Systems (ADAS). GPU-powered platforms are promising candidates for such computational tasks, however current low-level programming models challenge the accelerator software certification process, while they limit the hardware selection to a fraction of the available platforms. In this paper we present Brook Auto, a high-level programming language for automotive GPU systems which removes these limitations. We describe the challenges and solutions we faced in its implementation, as well as a complete evaluation in terms of performance and productivity, which shows the effectiveness of our method.

Previous works in the literature have shown the feasibility of general purpose computations for non-visual applications on low-end mobile graphics processors using graphics APIs. These works focused only on the functional aspects of the software, ignoring the implementation details and therefore their performance implications due to their particular micro-architecture. Since various steps in such applications can be implemented in multiple ways, we identify optimisation opportunities, explore the different options and evaluate them. We show that the implementation details can significantly affect the obtained performance with discrepancies up to 3 orders of magnitude and we demonstrate the effectiveness of our proposal on two embedded platforms, obtaining more than 16× speedup over benchmarks designed following OpenGL ES 2 best practices.

Modern and future automotive systems incorporate several Advanced Driving Assistance Systems (ADAS). Those systems require significant performance that cannot be provided with traditional automotive processors and programming models. Multicore CPUs and Nvidia GPUs using CUDA are currently considered by both automotive industry and research community to provide the necessary computational power. However, despite several recent published works in this domain, there is an absolute lack of open implementations of GPU-based ADAS software, that can be used for benchmarking candidate platforms. In this work, we present a multi-CPU and GPU implementation of an open implementation of a pedestrian detection benchmark based on the Viola-Jones image recognition algorithm. We present our optimization strategies and evaluate our implementation on a multiprocessor system featuring multiple GPUs, showing an overall 88.5× speedup over the sequential version.