Dynamic Interference-Sensitive Run-time Adaptation of Time-Triggered Schedules

Over-approximated Worst-Case Execution Time (WCET) estimations for multi-cores lead to safe, but over-provisioned, systems and underutilized cores. To reduce WCET pessimism, interference-sensitive WCET (isWCET) estimations are used. Although they provide tighter WCET bounds, they are valid only for a specific schedule solution. Existing approaches have to maintain this isWCET schedule solution at run-time, via time-triggered execution, in order to be safe. Hence, any earlier execution of tasks, enabled by adapting the isWCET schedule solution, is not possible. In this paper, we present a dynamic approach that safely adapts isWCET schedules during execution, by relaxing or completely removing isWCET schedule dependencies, depending on the progress of each core. In this way, an earlier task execution is enabled, creating time slack that can be used by safety-critical and mixed-criticality systems to provide higher Quality-of-Services or execute other best-effort applications. The Response-Time Analysis (RTA) of the proposed approach is presented, showing that although the approach is dynamic, it is fully predictable with bounded WCET. To support our contribution, we evaluate the behavior and the scalability of the proposed approach for different application types and execution configurations on the 8-core Texas Instruments TMS320C6678 platform, obtaining significant performance improvements compared to static approaches

Modeling of Reconfigurable Medical Ultrasonic Applications in BIP

Medical ultrasonic imaging applications require high quality of images produced in real-time often with limited resources available. Deadlock-freedom and confluency must be guaranteed to ensure the correctness of the applications, while feasibility and optimality properties are required to provide the best Quality of Service (QoS) within available resources. In this paper we introduce BIP (Behavior-Interaction-Priority) framework components as main building blocks to model such applications in a correct-by-construction manner. Based on those components we model a reconfigurable multi-mode processing pipeline for ultrasonic imaging that supports QoS management by topology reconfiguration. Finally, as a proof of concept, we present a simple quality controller as a well-triggered component, which when combined with the processing pipeline can manipulate the quality of image processing

Efficient Adaptive Hard Real-time Multi-processor Systems

Modern computing systems are based on multi-processor systems, i.e. multiple cores on the same chip. Hard real-time systems are required to perform particular tasks within certain amount of time; failure to do so characterises an unaccepted behavior. Hard real-time systems are found in safety-critical applications, e.g. airbag control software, flight control software, etc. In safety-critical applications, failure to meet the real-time constraints can have catastrophic effects. The safe and, at the same time, efficient deployment of applications, with hard real-time constraints, on multi-processors is a challenging task. Scheduling methods and Models of Computation, that provide safe deployments, require a realistic estimation of the Worst-Case Execution Time (WCET) of tasks. The simultaneous access of shared resources by parallel tasks, causes interference delays due to hardware arbitration. Interference delays can be accounted for, with the pessimistic assumption that all possible interference can happen. The resulting schedules would be exceedingly conservative, thus the benefits of multi-processor would be significantly negated. Producing less pessimistic schedules is challenging due to the inter-dependency between WCET estimation and deployment optimisation. Accurate estimation of interference delays -and thus estimation of task WCET- depends on the way an application is deployed; deployment is an optimisation problem that depends on the estimation of task WCET. Another efficiency gap, which is of consequence in several systems (e.g. airbag control), stems from the fact that rarely tasks execute with their WCET. Safe runtime adaptation based on the Actual Execution Times, can yield additional improvements in terms of latency (more responsive systems). To achieve efficiency and retain adaptability, we propose that interference analysis should be coupled with the deployment process. The proposed interference analysis method estimates the possible amount of interference, based on an architecture and an application model. As more information is provided, such as scheduling, memory mapping, etc, the per-task interference estimation becomes more accurate. Thus, the method computes interference-sensitive WCET estimations (isWCET). Based on the isWCET method, we propose a method to break the inter-dependency between WCET estimation and deployment optimisation. Initially, the isWCETs are over-approximated, by assuming worst-case interference, and a safe deployment is derived. Subsequently, the proposed method computes accurate isWCETs by spatio-temporal exclusion, i.e. excluding interferences from non-overlapping tasks that share resources (space). Based on accurate isWCETs, the deployment solution is improved to provide better latency guarantees. We also propose a distributed runtime adaptation technique, that aims to improve run-time latency. Using isWCET estimations restricts the possible adaptations, as an adaptation might increase the interference and violate the safety guarantees. The proposed technique introduces statically scheduling dependencies between tasks that prevent additional interference. At runtime, a self-timed scheduling policy that respects these dependencies, is applied, proven to be safe, and with minimal overhead. Experimental evaluation on Kalray MPPA-256 shows that our methods improve isWCET up to 36%, guaranteed latency up to 46%, runtime performance up to 42%, with a consolidated performance gain of 50%

Assessment of Image Quality vs. Computation Cost for Different Parameterizations of Ultrasound Imaging Pipelines

Ultrasound imaging is a technique widely used in medicine to visualize organs and other body structures, capturing their position, size, morphology and any pathological lesions. Its use is unfortunately limited to specialized centers with trained personnel, and it would be beneficial to expand its applicability to environments like on-the-sheld emergency response and family physician cabinets. This requires the development of new ultrasound platforms that must be faster, lower-power, easier to use, safe and reliable. One of the major challenges to be met is to dynamically manage a myriad of different imaging options and configuration parameters, which impact image quality and computation cost at the same time. Focusing on this challenge, in this paper we first give an overview of ultrasound imaging techniques and of their possible configuration and parametrization options. We then discuss the impact of these options on computation cost and image quality, showing outcomes from a prototype Matlab ultrasound imaging pipeline

Near-optimal Deployment of Dataflow Applications on Many-core Platforms with Real-time Guarantees

Safe and optimal deployment of data-streaming applications on many-core platforms requires the realistic estimation of task Worst-Case Execution Time (WCET). On the other hand, task WCET depends on the deployment solution, due to the varying number of interferences on shared resources, thus introducing a cyclic dependency. Moreover, WCET is still an over-approximation of the Actual Execution Time (AET), thus leaving room for run-time optimisation. In this paper we introduce an offline/online optimisation approach. In the offline phase, we first break the cyclic dependency and acquire safe and near-optimal solutions for tasks partitioning/placement, mapping, scheduling and buffer allocation. Then, we tighten the WCETs and update the scheduling function accordingly. In the online phase we introduce a safe distributed readjustment of the offline schedule, based on the AET. Experiments on a Kalray MPPA-256 platform show a tightening of the guaranteed latency up to 46% in the offline phase, and 41% latency reduction in the online phase. In total, we achieve more than 50% of latency reduction

Timely Fine-Grained Interference-Sensitive Run-Time Adaptation of Time-Triggered Schedules

International audienc

Worst-case execution time analysis for many-core architectures with NoC

The optimal deployment of data streaming applications onto multi-/many-core platform providing real-time guarantees requires to solve the application partitioning and placement, buffer allocation and task mapping and scheduling optimisation problem using the tasks Worst-Case Execution Time (WCET). In turn, tasks WCET varies due to interferences that tasks experience when accessing shared resources that depend on the solutions of the optimisation problem. In this paper we propose a detailed interference-based method that first over-approximates the WCET and then tightens it by pruning out the interferences from tasks not overlapping in time and memory. We prove that derived bounds are safe. We have found that interferences on average amount to 10% of WCET, and were able to improve latency guarantee up to 34%

Progress-aware Dynamic Slack Exploitation in Mixed-critical Systems: Work-in-Progress

International audienceMixed-critical systems consist of high criticality and low criticality applications. When a high criticality task exceeds its less pessimistic Worst Case Execution Time (WCET), the system switches mode and low criticality tasks are usually dropped. To postpone mode switch, existing approaches explore the slack, created dynamically due to an actual execution of a task that is faster than its WCET. However, the slack is visible only after the task has finished. To further enable dynamic slack exploitation, we propose a fine-grained approach that exposes the slack created due to the progress of tasks, during execution, and safely uses it to postpone mode switch

Mitigating Mode-Switch through Run-time Computation of Response Time

International audienceMixed-critical systems consist of applications with different criticality. In these systems, different confidence levels of Worst-Case Execution Time (WCET) estimations are used. Dual criticality systems use a less pessimistic, but with lower level of assurance, WCET estimation, and a safe, but pessimistic, WCET estimation. Initially, both high and low criticality tasks are executed. When a high criticality task exceeds its less pessimistic WCET, the system switches mode and low criticality tasks are usually dropped, reducing the overall system Quality of Service (QoS). To postpone mode switch, and thus, improve QoS, existing approaches explore the slack, created dynamically, when the actual execution of a task is faster than its WCET. However, existing approaches observe this slack only after the task has finished execution. To enhance dynamic slack exploitation, we propose a fine-grained approach that is able to expose the slack during the progress of a task, and safely uses it to postpone mode switch. The evaluation results show that the proposed approach has lower cost and achieves significant improvements in avoiding mode-switch, compared to existing approaches