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 HT r OP that is completely transparent to the user. HT r OP is able to automatically analyze a sequential CPU application, detect computational hotspots, and generate parallel OpenCL host and kernel code. The potential of HT r OP is demonstrated by offloading hotspots to different OpenCL-enabled resources (currently the CPU, the general-purpose 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.

Accelerators can offer exceptional performance advantages. However, programmers need to spend considerable efforts on acceleration, without knowing how sustainable the employed programming models, languages and tools are. To tackle this challenge, we propose and demonstrate a new runtime system called HTrOP that is able to automatically generate and execute OpenCL code from sequential CPU code. HTrOP transforms suitable data-parallel loops into independent OpenCL-typical work-items and handles concrete calls to these devices through a mix of library components and application-specific OpenCL host code. Computational hotspots are identified and can be offloaded to different resources (CPU, GPGPU and Xeon Phi). We demonstrate the potential of HTrOP on a broad set of applications and are able to improve the performance by 4.3X on average.

Modern computing systems featuring different kinds of processing elements have proven to be efficient in terms of performance/energy trade-offs. Furthermore these systems usually have to execute multiple concurrent tasks without any apriori knowledge on expected arrival times, in an unpredictable and very dynamic environment. This scenario has propelled an interest towards self-adaptive systems that dynamically reorganize the use of system resources to optimize for a given goal. The SAVE project will develop a Heterogeneous System Architecture that will decide at runtime to execute task on the appropriate kind of resources, based on the current requirements. This paper presents a first implementation of a resource allocation policy that dynamically shares heterogeneous resources between multiple running applications. Resource allocation mechanisms are discussed and evaluated in an experimental campaign, showing how the policy helps in attaining users' applications goals.

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 specification-time 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.