Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics

In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Orozco, Javier Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

Asignación de tareas a procesadores en un sistema distribuido de tiempo real duro utilizando algoritmos genéticos y lógica difusa

Se presenta un método basado en algoritmos genéticos para atacar el problema de asignación de un conjunto de tareas apropiativas, sobre un conjunto de procesadores distribuidos que deben trabajar en un entorno de tiempo real duro. Las tareas son cooperativas y utilizan como vía de comunicación una red local. Los coeficientes que ponderan la función de costo del algoritmo genético son calculados utilizando operadores difusos. Sobre el sistema existe un conjunto de restricciones que debe ser satisfecho para obtener una solución compatible con los requerimientos de tiempo real duro.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

Asignación de tareas a procesadores en un sistema distribuido de tiempo real duro utilizando algoritmos genéticos y lógica difusa

Se presenta un método basado en algoritmos genéticos para atacar el problema de asignación de un conjunto de tareas apropiativas, sobre un conjunto de procesadores distribuidos que deben trabajar en un entorno de tiempo real duro. Las tareas son cooperativas y utilizan como vía de comunicación una red local. Los coeficientes que ponderan la función de costo del algoritmo genético son calculados utilizando operadores difusos. Sobre el sistema existe un conjunto de restricciones que debe ser satisfecho para obtener una solución compatible con los requerimientos de tiempo real duro.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

Asignación de tareas a procesadores en un sistema distribuido de tiempo real duro utilizando algoritmos genéticos y lógica difusa

Se presenta un método basado en algoritmos genéticos para atacar el problema de asignación de un conjunto de tareas apropiativas, sobre un conjunto de procesadores distribuidos que deben trabajar en un entorno de tiempo real duro. Las tareas son cooperativas y utilizan como vía de comunicación una red local. Los coeficientes que ponderan la función de costo del algoritmo genético son calculados utilizando operadores difusos. Sobre el sistema existe un conjunto de restricciones que debe ser satisfecho para obtener una solución compatible con los requerimientos de tiempo real duro.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

Proceedings of Junior Researcher Workshop on Real-Time Computing

It is our great pleasure to welcome you to Junior Researcher Workshop on Real-Time Computing 2007, which is held conjointly with the 15th conference on Real-Time and Network Systems (RTNS'07). The first successful edition was held conjointly with the French Summer School on Real-Time Systems 2005 (http://etr05.loria.fr). Its main purpose is to bring together junior researchers (Ph.D. students, postdoc, ...) working on real-time systems. This workshop is a good opportunity to present our works and share ideas with other junior researchers and not only, since we will present our work to the audience of the main conference. In response to the call for papers, 14 papers were submitted and the international Program Committee provided detailed comments to improve these work-in-progress papers. We hope that our remarks will help the authors to submit improved long versions of theirs papers to the next edition of RTNS. JRWRTC'07 would not be possible without the generous contribution of many volunteers and institutions which supported RTNS'07. First, we would like to express our sincere gratitude to our sponsors for their financial support : Conseil Général de Meuthe et Moselle, Conseil Régional de Lorraine, Communauté Urbaine du Grand Nancy, Université Henri Poincaré, Institut National Polytechnique de Lorraine and LORIA and INRIA Lorraine. We are thankful to Pascal Mary for authorizing us to use his nice picture of “place Stanislas” for the proceedings and web site (many others are available at www.laplusbelleplacedumonde.com). Finally, we are most grateful to the local organizing committee that helped to organize the conference

Performance, Power Modeling and Optimization for High-Performance Computing Systems

University of Minnesota Ph.D. dissertation.October 2016. Major: Electrical/Computer Engineering. Advisor: John Sartori. 1 computer file (PDF); xi, 154 pages.Heterogeneity abounds in modern high-performance computing systems. Applications are heterogeneous, containing time-varying unbalanced utilization for different resources, and system architectures have become heterogeneous in order to achieve higher levels of performance and energy efficiency. The most powerful, and also the most energy-efficient high-performance computing systems today consist of many-core CPUs and GPGPUs with a variety of specialize on-chip and off-chip memories. These heterogeneous systems provide a huge amount of computing resources, but it is becoming increasingly challenging to use them effectively and efficiently to maximize their potential. This becomes an even more pressing challenge as energy efficiency becomes the primary barrier to achieving higher levels of performance. This thesis addresses the challenges of performance modeling and optimization in heterogeneous high-performance computing systems. Effective system optimization requires understanding of how performance and power change in response to optimizations. Therefore, we begin by summarizing the impact of modern architectural advances on performance and power modeling for chip multiprocessors (CMPs). We present two models that estimate the performance and power in such systems. The first model, CAMP, is a fast and accurate cache-aware performance model that estimates the performance degradation due to cache contention of processes running on cache-sharing cores. We then propose a system-level power model for a multi-programmed CMP environment that accounts for cache contention. We explain how to integrate the two models to enable power-aware process assignment. Then, we propose an off-chip memory access-aware runtime DVFS control technique that minimizes energy consumption subject to a constraint on application execution time. The second part of the dissertation focuses on improving performance for GPGPUs. After a thorough analysis on CPI breakdown, we lay out all the key factors that govern GPU throughput. In order to improve overall performance for GPGPUs, we propose two approaches that address the key factors, without introducing extra congestion and degradation to the system. We first propose a new two-level priority scheduling policy to improve overall performance by optimizing effective degree of parallelism. Then, we propose ICMT, a full, detailed solution for intra-core multitasking for GPGPUs, including architectural support and a contention-aware workload scheduling algorithm that improves all the key factors in a balanced fashion. Furthermore, we propose a new contention-aware analytical performance model that provides fine-grained workload scheduling decisions for intra-core multitasking, including detailed resource allocation from co-scheduled workloads

Instruction Cache Optimizations in Embedded Real-Time Systems

Ph.DDOCTOR OF PHILOSOPH

Operating System Contribution to Composable Timing Behaviour in High-Integrity Real-Time Systems

The development of High-Integrity Real-Time Systems has a high footprint in terms of human, material and schedule costs. Factoring functional, reusable logic in the application favors incremental development and contains costs. Yet, achieving incrementality in the timing behavior is a much harder problem. Complex features at all levels of the execution stack, aimed to boost average-case performance, exhibit timing behavior highly dependent on execution history, which wrecks time composability and incrementaility with it. Our goal here is to restitute time composability to the execution stack, working bottom up across it. We first characterize time composability without making assumptions on the system architecture or the software deployment to it. Later, we focus on the role played by the real-time operating system in our pursuit. Initially we consider single-core processors and, becoming less permissive on the admissible hardware features, we devise solutions that restore a convincing degree of time composability. To show what can be done for real, we developed TiCOS, an ARINC-compliant kernel, and re-designed ORK+, a kernel for Ada Ravenscar runtimes. In that work, we added support for limited-preemption to ORK+, an absolute premiere in the landscape of real-word kernels. Our implementation allows resource sharing to co-exist with limited-preemptive scheduling, which extends state of the art. We then turn our attention to multicore architectures, first considering partitioned systems, for which we achieve results close to those obtained for single-core processors. Subsequently, we shy away from the over-provision of those systems and consider less restrictive uses of homogeneous multiprocessors, where the scheduling algorithm is key to high schedulable utilization. To that end we single out RUN, a promising baseline, and extend it to SPRINT, which supports sporadic task sets, hence matches real-world industrial needs better. To corroborate our results we present findings from real-world case studies from avionic industry

Efficient Synchronization for Real-Time Systems with Nested Resource Access

Real-time systems are comprised of tasks, each of which must be guaranteed to meet its timing requirements. These tasks may request access to shared system components, called resources. Each such request may experience delays before being granted resource access. These delays can be separated into two categories: (i) those caused by the order in which tasks are granted resource access, and (ii) those caused by the time it takes to coordinate this ordering. If these delays are too large, a task may be unable to meet its timing requirements. Tasks can require access to multiple resources concurrently, acquiring these resources in a nested fashion. This nested resource access can cause significant delays to tasks; these delays can far exceed those when only a single resource is required at a time, as certain request orderings cause delays between tasks that do not share any resources. This dissertation presents locking protocols and a protocol-independent approach to mitigate resource access delays. Nested resource access can increase delays for all requests, including non-nested requests. The first protocol eliminates these additional delays by separating requests by type and creating a fast-path mechanism for non-nested requests. The next two protocols both reduce delays by reordering requests. One protocol reorders requests as they are issued, and the other uses an offline process to determine which requests may execute concurrently. These three protocols were compared to prior approaches in an evaluation across a range of task systems; all three protocols resulted in more task systems guaranteed to meet their timing requirements. Finally, a protocol-independent approach reduces delays by using a designated task to execute the locking protocol on behalf of other tasks. When applied to two protocol variants, this approach significantly reduced delays.Doctor of Philosoph