Different task models have been proposed to represent the parallel structure of real-time tasks executing on manycore platforms: fork/join, synchronous parallel, DAG-based, etc. Despite different schedulability tests and resource augmentation bounds are available for these task systems, we experience difficulties in applying such results to real application scenarios, where the execution flow of parallel tasks is characterized by multiple (and nested) conditional structures. When a conditional branch drives the number and size of sub-jobs to spawn, it is hard to decide which execution path to select for modeling the worst-case scenario. To circumvent this problem, we integrate control flow information in the task model, considering conditional parallel tasks (cp-tasks) represented by DAGs composed of both precedence and conditional edges. For this task model, we identify meaningful parameters that characterize the schedulability of the system, and derive efficient algorithms to compute them. A response time analysis based on these parameters is then presented for different scheduling policies. A set of simulations shows that the proposed approach allows efficiently checking the schedulability of the addressed systems, and that it significantly tightens the schedulability analysis of non-conditional (e.g., Classic DAG) tasks over existing approaches

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OpenMP is increasingly being supported by the newest high-end embedded many-core processors. Despite the lack of any notion of real-time execution, the latest specification of OpenMP (v4.0) introduces a tasking model that resembles the way real-time embedded applications are modeled and designed, i.e., as a set of periodic task graphs. This makes OpenMP4 a convenient candidate to be adopted in future real-time systems. However, OpenMP4 incorporates as well features to guarantee backward compatibility with previous versions that limit its practical usability in real-time systems. The most notable example is the distinction between tied and untied tasks. Tied tasks force all parts of a task to be executed on the same thread that started the execution, whereas a suspended untied task is allowed to resume execution on a different thread. Moreover, tied tasks are forbidden to be scheduled in threads in which other non-descendant tied tasks are suspended. As a result, the execution model of tied tasks, which is the default model in OpenMP to simplify the coexistence with legacy constructs, clearly restricts the performance and has serious implications on the response time analysis of OpenMP4 applications, making difficult to adopt it in real-time environments. In this paper, we revisit OpenMP design choices, introducing timing predictability as a new and key metric of interest. Our first results confirm that even if tied tasks can be timing analyzed, the quality of the analysis is much worse than with untied tasks. We thus reason about the benefits of using untied tasks, deriving a response time analysis for this model, and so allowing OpenMP4 untied model to be applied to real-time systems

Abstract-Limited preemptive (LP) scheduling has been demonstrated to effectively improve the schedulability of fully preemptive (FP) and fully non-preemptive (FNP) paradigms. On one side, LP reduces the preemption related overheads of FP; on the other side, it restricts the blocking effects of FNP. However, LP has been applied to multi-core scenarios only when completely sequential task systems are considered. This paper extends the current state-of-the-art response time analysis for global fixed priority scheduling with fixed preemption points by deriving a new response time analysis for DAG-based task-sets.

Several task models have been introduced in the literature to describe the intrinsic parallelism of real-time activities, including fork/join, synchronous parallel, DAG-based, etc. Although schedulability tests and resource augmentation bounds have been derived for these task models in the context of multicore systems, they are still too pessimistic to describe the execution flow of parallel tasks characterized by multiple (and nested) conditional statements, where it is hard to decide which execution path to select for modeling the worst-case scenario. To overcome this problem, this paper proposes a task model that integrates control flow information by considering conditional parallel tasks (cp-tasks) represented by DAGs with both precedence and conditional edges. For this task model, a set of meaningful parameters are identified and computed by efficient algorithms and a response-time analysis is presented for different scheduling policies. Experimental results are finally reported to evaluate the efficiency of the proposed schedulability tests and their performance with respect to classic tests based on both conditional and non-conditional existing approaches

Background: Over the past ten years, the authors have been designing, developing, and testing pervasive technology to support children with autism (ASD). Methods: In the present study, an integrated system based on multimedia and augmented interaction technologies have been tested on young subjects with ASD and dyspraxia in the age range of 6–10 years, in charge for rehabilitation treatments; a team of clinical psychologists has analyzed the results of the experimentation. The ten children involved in the project underwent an initial assessment of praxis skills and motor coordination. Subsequently, the subjects were subdivided into two subgroups: five children participated in the experimentation and five were evaluated as the control group (treatment as usual). Results: The evaluation showed an increased score in the several aspects considered, and particularly those related to motor coordination. An improvement in balancing tests and in hands-movement testing was found. Conclusion: The children involved in the sessions showed greater ability to self-control the movement as well as to select specific motor areas. The methods used seem to be promising to improve emotional and social skills too in a motivating and enjoyable climate. A high level of acceptance by professionals was observed and parents’ feedback was also positive.

Abstract-Parallel computation is fundamental to satisfy the performance requirements of advanced safety-critical systems. OpenMP is a good candidate to exploit the performance opportunities of parallel platforms. However, safety-critical systems are often based on static allocation strategies, whereas current OpenMP implementations are based on dynamic schedulers. This paper proposes two OpenMP-compliant static allocation approaches: an optimal but costly approach based on an ILP formulation, and a sub-optimal but tractable approach that computes a worst-case makespan bound close to the optimal one.

A major obstacle towards the adoption of multi-core platforms for real-time systems is given by the difficulties in characterizing the interference due to memory contention. The simple fact that multiple cores may simultaneously access shared memory and communication resources introduces a significant pessimism in the timing and schedulability analysis. To counter this problem, predictable execution models have been proposed splitting task executions into two consecutive phases: a memory phase in which the required instruction and data are pre-fetched to local memory (M-phase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but it also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. In this paper, we take a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the M/C (memory-computation) model. We provide a theoretical framework that aims at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on a single-core systems. We identify a tight critical instant for M/C tasks scheduled with fixed priority, providing an exact response-time analysis with pseudo-polynomial complexity. We show in our experiments that a significant schedulability improvement may be obtained with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems. © 2015 ACM