Solving partial differential equations on unstructured grids is a cornerstone of engineering and scientific computing. Nowadays, heterogeneous parallel platforms with CPUs, GPUs, and FPGAs enable energy-efficient and computationally demanding simulations. We developed the HighPerMeshes C++-embedded Domain-Specific Language (DSL) for bridging the abstraction gap between the mathematical and algorithmic formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different parallel programming and runtime models on the other hand. Thus, the HighPerMeshes DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HighPerMeshes DSL, and demonstrate its usage with three examples, a Poisson and monodomain problem, respectively, solved by the continuous finite element method, and the discontinuous Galerkin method for Maxwell’s equation. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented. Finally, the achievable performance and scalability are demonstrated for a typical example problem on a multi-core CPU cluster.

In this chapter, we present an introduction to the ReconOS operating system for reconfigurable computing. ReconOS offers a unified multi-threaded programming model and operating system services for threads executing in software and threads mapped to reconfigurable hardware. By supporting standard POSIX operating system functions for both software and hardware threads, ReconOS particularly caters to developers with a software background, because developers can use well-known mechanisms such as semaphores, mutexes, condition variables, and message queues for developing hybrid applications with threads running on the CPU and FPGA concurrently. Through the semantic integration of hardware accelerators into a standard operating system environment, ReconOS allows for rapid design space exploration, supports a structured application development process and improves the portability of applications between different reconfigurable computing systems.

Many modern compute nodes are heterogeneous multi-cores that integrate several CPU cores with fixed function or reconfigurable hardware cores. Such systems need to adapt task scheduling and mapping to optimise for performance and energy under varying workloads and, increasingly important, for thermal and fault management and are thus relevant targets for self-aware computing. In this chapter, we take up the generic reference architecture for designing self-aware and self-expressive computing systems and refine it for heterogeneous multi-cores. We present ReconOS, an architecture, programming model and execution environment for heterogeneous multi-cores, and show how the components of the reference architecture can be implemented on top of ReconOS. In particular, the unique feature of dynamic partial reconfiguration supports self-expression through starting and terminating reconfigurable hardware cores. We detail a case study that runs two applications on an architecture with one CPU and 12 reconfigurable hardware cores and present self-expression strategies for adapting under performance, temperature and even conflicting constraints. The case study demonstrates that the reference architecture as a model for self-aware computing is highly useful as it allows us to structure and simplify the design process, which will be essential for designing complex future compute nodes. Furthermore, ReconOS is used as a base technology for flexible protocol stacks in Chapter 10, an approach for self-aware computing at the networking level.

Im Bereich der Computersysteme ist die Festlegung der Grenze zwischen Hardware und Software eine zentrale Problemstellung. Diese Grenze hat in den letzten Jahrzehnten nicht nur die Entwicklung von Computersystemen bestimmt, sondern auch die Strukturierung der Ausbildung in den Computerwissenschaften beeinflusst und sogar zur Entstehung von neuen Forschungsrichtungen gef{\"u}hrt. In diesem Beitrag besch{\"a}ftigen wir uns mit Verschiebungen an der Grenze zwischen Hardware und Software und diskutieren insgesamt drei qualitativ unterschiedliche Formen solcher Verschiebungen. Wir beginnen mit der Entwicklung von Computersystemen im letzten Jahrhundert und der Entstehung dieser Grenze, die Hardware und Software erst als eigenst{\"a}ndige Produkte differenziert. Dann widmen wir uns der Frage, welche Funktionen in einem Computersystem besser in Hardware und welche besser in Software realisiert werden sollten, eine Fragestellung die zu Beginn der 90er-Jahre zur Bildung einer eigenen Forschungsrichtung, dem sogenannten Hardware/Software Co-design, gef{\"u}hrt hat. Im Hardware/Software Co-design findet eine Verschiebung von Funktionen an der Grenze zwischen Hardware und Software w{\"a}hrend der Entwicklung eines Produktes statt, um Produkteigenschaften zu optimieren. Im fertig entwickelten und eingesetzten Produkt hingegen k{\"o}nnen wir dann eine feste Grenze zwischen Hardware und Software beobachten. Im dritten Teil dieses Beitrags stellen wir mit selbst-adaptiven Systemen eine hochaktuelle Forschungsrichtung vor. In unserem Kontext bedeutet Selbstadaption, dass ein System Verschiebungen von Funktionen an der Grenze zwischen Hardware und Software autonom w{\"a}hrend der Betriebszeit vornimmt. Solche Systeme beruhen auf rekonfigurierbarer Hardware, einer relativ neuen Technologie mit der die Hardware eines Computers w{\"a}hrend der Laufzeit ver{\"a}ndert werden kann. Diese Technologie f{\"u}hrt zu einer durchl{\"a}ssigen Grenze zwischen Hardware und Software bzw. l{\"o}st sie die herk{\"o}mmliche Vorstellung einer festen Hardware und einer flexiblen Software damit auf.

In this paper, we describe the architecture of the virtual resource manager VRM, a management system designed to reside on top of local resource management systems for cluster computers and other kinds of resources. The most important feature of the VRM is its capability to handle quality-of-service (QoS) guarantees and service-level agreements (SLAs). The particular emphasis of the paper is on the various opportunities to deal with local autonomy for resource management systems not supporting SLAs. As local administrators may not want to hand over complete control to the Grid management, it is necessary to define strategies that deal with this issue. Local autonomy should be retained as much as possible while providing reliability and QoS guarantees for Grid applications, e.g., specified as SLAs.

Grid Computing promises an efficient sharing of world-wide distributed resources, ranging from hardware, software, expert knowledge to special I/O devices. However, although the main Grid mechanisms are already developed or are currently addressed by tremendous research effort, the Grid environment still suffers from a low acceptance in different user communities. Beside difficulties regarding an intuitive and comfortable resource access, various problems related to the reliability and the Quality-of-Service while using the Grid exist. Users should be able to rely, that their jobs will have certain priority at the remote Grid site and that they will be finished upon the agreed time regardless of any provider problems. Therefore, QoS issues have to be considered in the Grid middleware but also in the local resource management systems at the Grid sites. However, most of the currently used resource management systems are not suitable for SLAs, as they do not support resource reservation and do not offer mechanisms for job checkpointing/migration respectively. The latter are mandatory for Grid providers as rescue anchor in case of system failures or system overload. This paper focuses on SLA-aware job migration and presents a work, which is being performed in the EU supported project HPC4U.

The growing maturity of hardware and software components has tempted researchers to build very large SCI clusters with several hundred processors that are operated as high-performance compute servers in multi-user mode. In this chapter, we present a resource management software for the user access and system administration of high-performance compute clusters named Computing Center Software (CCS). It is in day-to-day use since 1992 on various parallel systems and has recently been adapted to the management of SCI clusters. CCS provides pluggable schedulers, optimal space partitioning for multiple users, reliable user access, and powerful tools for specifying resources and services by means of a specification language and a graphical user interface. After a brief introduction in the remainder of this section, we describe the CCS system architecture and the characteristics of its resource description facilities.

With a steadily increasing number of services, metacomputing is now gaining importance in science and industry. Virtual organizations, autonomous agents, mobile computing services, and high-performance client–server applications are among the many examples of metacomputing services. For all of them, resource description plays a major role in organizing access, use, and administration of the computing components and software services. We present a generic Resource and Service Description (RSD) for specifying the hardware and software components of (meta-) computing environments. Its graphical interface allows metacomputer users to specify their resource requests. Its textual counterpart gives service providers the necessary flexibility to specify topology and properties of the available system and software resources. Finally, its internal object-oriented representation is used to link different resource management systems and service tools. With these three representations, our generic RSD approach is a key component for building metacomputer environments.