151 research outputs found
Schedulability analysis of global scheduling algorithms on multiprocessor platforms
This paper addresses the schedulability problem of periodic and sporadic real-time task sets with constrained deadlines preemptively scheduled on a multiprocessor platform composed by identical processors. We assume that a global work-conserving scheduler is used and migration from one processor to another is allowed during a task lifetime. First, a general method to derive schedulability conditions for multiprocessor real-time systems will be presented. The analysis will be applied to two typical scheduling algorithms: earliest deadline first (EDF) and fixed priority (FP). Then, the derived schedulability conditions will be tightened, refining the analysis with a simple and effective technique that significantly improves the percentage of accepted task sets. The effectiveness of the proposed test is shown through an extensive set of synthetic experiments
Convolutional Neural Networks on Embedded Automotive Platforms: A Qualitative Comparison
In the last decade, the rise of power-efficient, het-
erogeneous embedded platforms paved the way to the effective
adoption of neural networks in several application domains.
Especially, many-core accelerators (e.g., GPUs and FPGAs) are
used to run Convolutional Neural Networks, e.g., in autonomous
vehicles, and industry 4.0. At the same time, advanced research
on neural networks is producing interesting results in computer
vision applications, and NN packages for computer vision object
detection and categorization such as YOLO, GoogleNet and
AlexNet reached an unprecedented level of accuracy and perfor-
mance. With this work, we aim at validating the effectiveness and
efficiency of most recent networks on state-of-the-art embedded
platforms, with commercial-off-the-shelf System-on-Chips such
as the NVIDIA Tegra X2 and Xilinx Ultrascale+. In our vision,
this work will support the choice of the most appropriate CNN
package and computing system, and at the same time tries to
“make some order” in the field
Schedulability analysis of global scheduling algorithms on multiprocessor platforms
This paper addresses the schedulability problem of periodic and sporadic real-time task sets with constrained deadlines preemptively scheduled on a multiprocessor platform composed by identical processors. We assume that a global work-conserving scheduler is used and migration from one processor to another is allowed during a task lifetime. First, a general method to derive schedulability conditions for multiprocessor real-time systems will be presented. The analysis will be applied to two typical scheduling algorithms: earliest deadline first (EDF) and fixed priority (FP). Then, the derived schedulability conditions will be tightened, refining the analysis with a simple and effective technique that significantly improves the percentage of accepted task sets. The effectiveness of the proposed test is shown through an extensive set of synthetic experiments
Work-in-Progress: NVIDIA GPU Scheduling Details in Virtualized Environments
Modern automotive grade embedded platforms feature high performance Graphics Processing Units (GPUs) to support the massively parallel processing power needed for next-generation autonomous driving applications. Hence, a GPU scheduling approach with strong Real-Time guarantees is needed. While previous research efforts focused on reverse engineering the GPU ecosystem in order to understand and control GPU scheduling on NVIDIA platforms, we provide an in depth explanation of the NVIDIA standard approach to GPU application scheduling on a Drive PX platform. Then, we discuss how a privileged scheduling server can be used to enforce arbitrary scheduling policies in a virtualized environment
A C-DAG task model for scheduling complex real-time tasks on heterogeneous platforms: preemption matters
Recent commercial hardware platforms for embedded real-time systems feature
heterogeneous processing units and computing accelerators on the same
System-on-Chip. When designing complex real-time application for such
architectures, the designer needs to make a number of difficult choices: on
which processor should a certain task be implemented? Should a component be
implemented in parallel or sequentially? These choices may have a great impact
on feasibility, as the difference in the processor internal architectures
impact on the tasks' execution time and preemption cost. To help the designer
explore the wide space of design choices and tune the scheduling parameters, in
this paper we propose a novel real-time application model, called C-DAG,
specifically conceived for heterogeneous platforms. A C-DAG allows to specify
alternative implementations of the same component of an application for
different processing engines to be selected off-line, as well as conditional
branches to model if-then-else statements to be selected at run-time. We also
propose a schedulability analysis for the C-DAG model and a heuristic
allocation algorithm so that all deadlines are respected. Our analysis takes
into account the cost of preempting a task, which can be non-negligible on
certain processors. We demonstrate the effectiveness of our approach on a large
set of synthetic experiments by comparing with state of the art algorithms in
the literature
The Parallel Supply Function Abstraction for a Virtual Multiprocessor
A new abstraction --- the Parallel Supply Function (PSF) --- is
proposed for representing the computing capabilities offered by
virtual platforms implemented atop identical multiprocessors. It is
shown that this abstraction is strictly more powerful than
previously-proposed ones, from the perspective of more accurately
representing the inherent parallelism of the provided computing
capabilities. Sufficient tests are derived for determining whether
a given real-time task system, represented as a collection of
sporadic tasks, is guaranteed
to always meet all
deadlines when scheduled upon a specified virtual platform using the
global EDF scheduling algorithm
The Multy Supply Function Abstraction for Multiprocessors
Multi-core platforms are becoming the dominant computing architecture for next generation embedded systems. Nevertheless, designing, programming, and analyzing such systems is not easy and a solid methodology is still missing. In this paper, we propose two powerful abstractions to model the computing power of a parallel machine, which provide a general interface for developing and analyzing real-time applications in isolation, independently of the physical platform. The proposed abstractions can be applied on top of different types of service mechanisms, such as periodic servers, static partitions, and P-fair time partitions. In addition, we developed the schedulability analysis of a set of real-time tasks on top of a parallel machine that is compliant with the proposed abstractions
On the compatibility of exact schedulability tests for global fixed priority pre-emptive scheduling with Audsley’s optimal priority assignment algorithm
Audsley's optimal priority assignment (OPA) algorithm can be applied to multiprocessor scheduling provided that three conditions hold with respect to the schedulability tests used. In this short paper, we prove that no exact test for global fixed priority pre-emptive scheduling of sporadic tasks can be compatible with Audsley's algorithm, and hence the OPA algorithm cannot be used to obtain an optimal priority assignment for such systems
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