An Analysis of Lazy and Eager Limited Preemption Approaches under DAG-Based Global Fixed Priority Scheduling

DAG-based scheduling models have been shown to effectively express the parallel execution of current many-core heterogeneous architectures. However, their applicability to real-time settings is limited by the difficulties to find tight estimations of the worst-case timing parameters of tasks that may arbitrarily be preempted/migrated at any instruction. An efficient approach to increase the system predictability is to limit task preemptions to a set of pre-defined points. This limited preemption model supports two different preemption approaches, eager and lazy, which have been analyzed only for sequential task-sets. This paper proposes a new response time analysis that computes an upper bound on the lower priority blocking that each task may incur with eager and lazy preemptions. We evaluate our analysis with both, synthetic DAG-based task-sets and a real case-study from the automotive domain. Results from the analysis demonstrate that, despite the eager approach generates a higher number of priority inversions, the blocking impact is generally smaller than in the lazy approach, leading to a better schedulability performance.This work was funded by the EU projects P-SOCRATES (FP7-ICT-2013-10) and HERCULES (H2020/ICT/2015/688860), and the Spanish Ministry of Science and Innovation under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

A time-predictable parallel programing model for real-time systems

The recent technological advancements and market trends are causing an interesting phenomenon towards the convergence of the high-performance and the embedded computing domains. Critical real-time embedded systems are increasingly concerned with providing higher performance to implement advanced functionalities in a predictable way. OpenMP, the de-facto parallel programming model for shared memory architectures in the high-performance computing domain, is gaining the attention to be used in embedded platforms. The reason is that OpenMP is a mature language that allows to efficiently exploit the huge computational capabilities of parallel embedded architectures. Moreover, OpenMP allows to express parallelism on top of the current technologies used in embedded designs (e.g., C/C++ applications). At a lower level, OpenMP provides a powerful task-centric model that allows to define very sophisticated types of regular and irregular parallelism. While OpenMP provides relevant features for embedded systems, both the programming interface and the execution model are completely agnostic to the timing requirements of real-time systems. This thesis evaluates the use of OpenMP to develop future critical real-time embedded systems. The first contribution analyzes the OpenMP specification from a timing perspective. It proposes new features to be incorporated in the OpenMP standard and a set of guidelines to implement critical real-time systems with OpenMP. The second contribution develops new methods to analyze and predict the timing behavior of parallel applications, so that the notion of parallelism can be safely incorporated into critical real-time systems. Finally, the proposed techniques are evaluated with both synthetic applications and real use cases parallelized with OpenMP. With the above contributions, this thesis pushes the limits of the use of task-based parallel programming models in general, and OpenMP in particular, in critical real-time embedded domains.Los recientes avances tecnológicos y tendencias de mercado estan causando un interesante fenómeno hacia la convergencia de dos dominios: la computacion de altas prestaciones y la computacion embebida. Hay cada vez mas interés en que los sistemas embebidos criticos de tiempo real proporcionen un mayor rendimiento para implementar funcionalidades avanzadas de una manera predecible. OpenMP, el modelo de programación paralela estándar para arquitecturas de memoria compartida en el dominio de la computación de altas prestaciones, está ganando atención para ser utilizado en systemas embebidos. La razón es que OpenMP es un lenguaje asentado que permite explotar eficientemente las enormes capacidades computacionales de las arquitecturas paralelas embebidas. Además, OpenMP permite expresar paralelismo sobre las tecnologías actuales utilizadas en los diseños embebidos (por ejemplo, aplicaciones C/C++). A un nivel inferior, OpenMP proporciona un potente modelo centrado en tareas que permite expresar tipos muy sofisticados de paralelismo regular e irregular. Si bien OpenMP proporciona funciones relevantes para los sistemas embebidos, tanto la interfaz de programación como el modelo de ejecución son completamente ajenos a los requisitos temporales de los sistemas de tiempo real. Esta tesis evalúa el uso de OpenMP para desarrollar los futuros sistemas embebidos criticos de timepo real. La primera contribución analiza la especificación de OpenMP desde una perspectiva temporal. Se propone nuevas funcionalidades que podrian ser incorporadas en el estándar de OpenMP y un conjunto de pautas para implementar sistemas críticos de tiempo real con OpenMP. La segunda contribución desarrolla nuevos métodos para analizar y predecir el comportamiento temporal de las aplicaciones paralelas, de modo que la noción de paralelismo se pueda incorporar de manera segura en sistemas críticos de tiempo real. Finalmente, las técnicas propuestas se evaluan con aplicaciones sintéticas y casos de uso reales paralelizados con OpenMP. Con las mencionadas contribuciones, esta tesis amplía los límites en el uso de los modelos de programación paralela basados en tarea en general, y de OpenMP en particular, en dominios embebidos criticos de tiempo real

Towards an OpenMP Specification for Critical Real-Time Systems

OpenMP is increasingly being considered as a convenient parallel programming model to cope with the performance requirements of critical real-time systems. Recent works demonstrate that OpenMP enables to derive guarantees on the functional and timing behavior of the system, a fundamental requirement of such systems. These works, however, focus only on the exploitation of fine grain parallelism and do not take into account the peculiarities of critical real-time systems, commonly composed of a set of concurrent functionalities. OpenMP allows exploiting the parallelism exposed within real-time tasks and among them. This paper analyzes the challenges of combining the concurrency model of real-time tasks with the parallel model of OpenMP. We demonstrate that OpenMP is suitable to develop advanced critical real-time systems by virtue of few changes on the specification, which allow the scheduling behavior desired (regarding execution priorities, preemption, migration and allocation strategies) in such systems.The research leading to these results has received funding from the Spanish Ministry of Science and Innovation, under contract TIN2015-65316-P, and from the European Union's Horizon 2020 Programme under the CLASS Project (www.classproject. eu), grant agreement No 780622.Peer ReviewedPostprint (author's final draft

The cooperative parallel: A discussion about run-time schedulers for nested parallelism

Nested parallelism is a well-known parallelization strategy to exploit irregular parallelism in HPC applications. This strategy also fits in critical real-time embedded systems, composed of a set of concurrent functionalities. In this case, nested parallelism can be used to further exploit the parallelism of each functionality. However, current run-time implementations of nested parallelism can produce inefficiencies and load imbalance. Moreover, in critical real-time embedded systems, it may lead to incorrect executions due to, for instance, a work non-conserving scheduler. In both cases, the reason is that the teams of OpenMP threads are a black-box for the scheduler, i.e., the scheduler that assigns OpenMP threads and tasks to the set of available computing resources is agnostic to the internal execution of each team. This paper proposes a new run-time scheduler that considers dynamic information of the OpenMP threads and tasks running within several concurrent teams, i.e., concurrent parallel regions. This information may include the existence of OpenMP threads waiting in a barrier and the priority of tasks ready to execute. By making the concurrent parallel regions to cooperate, the shared computing resources can be better controlled and a work conserving and priority driven scheduler can be guaranteed.Peer ReviewedPostprint (author's final draft

Response-Time Analysis of Limited-Preemptive Parallel DAG Tasks Under Global Scheduling

Most recurrent real-time applications can be modeled as a set of sequential code segments (or blocks) that must be (repeatedly) executed in a specific order. This paper provides a schedulability analysis for such systems modeled as a set of parallel DAG tasks executed under any limited-preemptive global job-level fixed priority scheduling policy. More precisely, we derive response-time bounds for a set of jobs subject to precedence constraints, release jitter, and execution-time uncertainty, which enables support for a wide variety of parallel, limited-preemptive execution models (e.g., periodic DAG tasks, transactional tasks, generalized multi-frame tasks, etc.). Our analysis explores the space of all possible schedules using a powerful new state abstraction and state-pruning technique. An empirical evaluation shows the analysis to identify between 10 to 90 percentage points more schedulable task sets than the state-of-the-art schedulability test for limited-preemptive sporadic DAG tasks. It scales to systems of up to 64 cores with 20 DAG tasks. Moreover, while our analysis is almost as accurate as the state-of-the-art exact schedulability test based on model checking (for sequential non-preemptive tasks), it is three orders of magnitude faster and hence capable of analyzing task sets with more than 60 tasks on 8 cores in a few seconds

High-Performance and Time-Predictable Embedded Computing

Nowadays, the prevalence of computing systems in our lives is so ubiquitous that we live in a cyber-physical world dominated by computer systems, from pacemakers to cars and airplanes. These systems demand for more computational performance to process large amounts of data from multiple data sources with guaranteed processing times. Actuating outside of the required timing bounds may cause the failure of the system, being vital for systems like planes, cars, business monitoring, e-trading, etc. High-Performance and Time-Predictable Embedded Computing presents recent advances in software architecture and tools to support such complex systems, enabling the design of embedded computing devices which are able to deliver high-performance whilst guaranteeing the application required timing bounds. Technical topics discussed in the book include: Parallel embedded platforms Programming models Mapping and scheduling of parallel computations Timing and schedulability analysis Runtimes and operating systems The work reflected in this book was done in the scope of the European project P SOCRATES, funded under the FP7 framework program of the European Commission. High-performance and time-predictable embedded computing is ideal for personnel in computer/communication/embedded industries as well as academic staff and master/research students in computer science, embedded systems, cyber-physical systems and internet-of-things.info:eu-repo/semantics/publishedVersio

High Performance Embedded Computing

Nowadays, the prevalence of computing systems in our lives is so ubiquitous that we live in a cyber-physical world dominated by computer systems, from pacemakers to cars and airplanes. These systems demand for more computational performance to process large amounts of data from multiple data sources with guaranteed processing times. Actuating outside of the required timing bounds may cause the failure of the system, being vital for systems like planes, cars, business monitoring, e-trading, etc. High-Performance and Time-Predictable Embedded Computing presents recent advances in software architecture and tools to support such complex systems, enabling the design of embedded computing devices which are able to deliver high-performance whilst guaranteeing the application required timing bounds. Technical topics discussed in the book include: Parallel embedded platforms Programming models Mapping and scheduling of parallel computations Timing and schedulability analysis Runtimes and operating systemsThe work reflected in this book was done in the scope of the European project P SOCRATES, funded under the FP7 framework program of the European Commission. High-performance and time-predictable embedded computing is ideal for personnel in computer/communication/embedded industries as well as academic staff and master/research students in computer science, embedded systems, cyber-physical systems and internet-of-things

Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks

Response-Time Analysis for Non-Preemptive Periodic Moldable Gang Tasks

Gang scheduling has long been adopted by the high-performance computing community as a way to reduce the synchronization overhead between related threads. It allows for several threads to execute in lock steps without suffering from long busy-wait periods or be penalized by large context-switch overheads. When combined with non-preemptive execution, gang scheduling significantly reduces the execution time of threads that work on the same data by decreasing the number of memory transactions required to load or store the data. In this work, we focus on two main types of gang tasks: rigid and moldable. A moldable gang task has a presumed known minimum and maximum number of cores on which it can be executed at runtime, while a rigid gang task always executes on the same number of cores. This work presents the first response-time analysis for non-preemptive moldable gang tasks. Our analysis is based on the notion of schedule abstraction; a new approach for response-time analysis with the promise of high accuracy. Our experiments on periodic rigid gang tasks show that our analysis is 4.9 times more successful in identifying schedulable tasks than the existing utilization-based test for rigid gang tasks.</p