Abstract. Feldspar is a domain specific language, embedded in Haskell, for programming digital signal processing algorithms. The final aim of a Feldspar program is to generate low level code with good performance. Still, we chose to provide the user with a purely functional DSL. The language is implemented as a minimal, deeply embedded core language, with shallow extensions built upon it. This paper presents full details of the essential parts of the implementation. Our initial conclusion is that this approach works well in our domain, although much work remains. 1

Graphics Processing Units (GPUs) are evolving into powerful general purpose computing platforms. At first, GPU performance was driven by the requirements of 3D graphics computer games. To fit this workload, a GPU is a many-core processor suitable for the data-parallel programming paradigm. Today, GPUs come with hundreds of processing elements and a theoretical single precision floating point performance in the teraflop range. Because of the computing power of modern GPUs, programmers are increasingly interested in making use of them for non-graphics applications. This desire has given rise to the research field that studies General Purpose Computations on GPUs (GPGPU). The manufacturers of GPUs are also acknowledging this trend and are tailoring their GPUs to meet both the desires of those playing games and the GPGPU community. CUDA is NVIDIA’s tool-set for GPGPU programming on their GPUs. CUDA is a big improvement for the GPGPU programmer compared to what was available before. In the early days, the GPGPU programmer was forced to express the algorithm being implemented as a computer graphics computation. CUDA provides a C compiler and a set

Abstract. We address the problem of runtime monitoring for hard realtime programs—a domain in which correctness is critical yet has largely been overlooked in the runtime monitoring community. We describe the challenges to runtime monitoring for this domain as well as an approach to satisfy the challenges. The core of our approach is a language and compiler called Copilot. Copilot is a stream-based dataflow language that generates small constant-time and constant-space C programs, implementing embedded monitors. Copilot also generates its own scheduler, obviating the need for an underlying real-time operating system. 1

Abstract. Runtime verification (RV) is a natural fit for ultra-critical systems, where correctness is imperative. In ultra-critical systems, even if the software is fault-free, because of the inherent unreliability of commodity hardware and the adversity of operational environments, processing units (and their hosted software) are replicated, and fault-tolerant algorithms are used to compare the outputs. We investigate both software monitoring in distributed fault-tolerant systems, as well as implementing fault-tolerance mechanisms using RV techniques. We describe the Copilot language and compiler, specifically designed for generating monitors for distributed, hard real-time systems, and we describe a case study in a Byzantine fault-tolerant airspeed sensor system. 1