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IGNACIO SANUDO OLMEDO

COLLABORATORE IN SPIN OFF
Dipartimento di Scienze Fisiche, Informatiche e Matematiche sede ex-Matematica

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Pubblicazioni

2022 - Real-Time Requirements for ADAS Platforms Featuring Shared Memory Hierarchies [Articolo su rivista]
Capodieci, Nicola; Burgio, Paolo; Cavicchioli, Roberto; Olmedo, Ignacio Sanudo; Solieri, Marco; Bertogna, Marko
abstract

2020 - The Key Role of Memory in Next-Generation Embedded Systems for Military Applications [Relazione in Atti di Convegno]
Sañudo, Ignacio; Cortimiglia, Paolo; Miccio, Luca; Solieri, Marco; Burgio, Paolo; Di Biagio, Christian; Felici, Franco; Nuzzo, Giovanni; Bertogna, Marko
abstract

With the increasing use of multi-core platforms in safety-related domains, aircraft system integrators and authorities exhibit a concern about the impact of concurrent access to shared-resources in the Worst-Case Execution Time (WCET). This paper highlights the need for accurate memory-centric scheduling mechanisms for guaranteeing prioritized memory accesses to Real-Time safety-related components of the system. We implemented a software technique called cache coloring that demonstrates that isolation at timing and spatial level can be achieved by managing the lines that can be evicted in the cache. In order to show the effectiveness of this technique, the timing properties of a real application are considered as a use case, this application is made of parallel tasks that show different trade-offs between computation and memory loads.

2019 - System Performance Modelling of Heterogeneous HW Platforms: An Automated Driving Case Study [Relazione in Atti di Convegno]
Wurst, F.; Dasari, D.; Hamann, A.; Ziegenbein, D.; Sanudo, I.; Capodieci, N.; Bertogna, M.; Burgio, P.
abstract

The push towards automated and connected driving functionalities mandates the use of heterogeneous HW platforms in order to provide the required computational resources. For these platforms, the established methods for performance modelling in industry are no longer effective. In this paper, we propose an initial modelling concept for heterogeneous platforms which can then be fed into appropriate tools to derive effective performance predictions. The approach is demonstrated for a prototypical automated driving application on the Nvidia Tegra X2 platform.

2018 - A Perspective on Safety and Real-Time Issues for GPU Accelerated ADAS [Relazione in Atti di Convegno]
Sanudo Olmedo, Ignacio; Capodieci, Nicola; Cavicchioli, Roberto
abstract

The current trend in designing Advanced Driving Assistance System (ADAS) is to enhance their computing power by using modern multi/many core accelerators. For many critical applications such as pedestrian detection, line following, and path planning the Graphic Processing Unit (GPU) is the most popular choice for obtaining orders of magnitude increases in performance at modest power consumption. This is made possible by exploiting the general purpose nature of today's GPUs, as such devices are known to express unprecedented performance per watt on generic embarrassingly parallel workloads (as opposed of just graphical rendering, as GPUs where only designed to sustain in previous generations). In this work, we explore novel challenges that system engineers have to face in terms of real-time constraints and functional safety when the GPU is the chosen accelerator. More specifically, we investigate how much of the adopted safety standards currently applied for traditional platforms can be translated to a GPU accelerated platform used in critical scenarios.

2018 - A survey on shared disk I/O management in virtualized environments under real time constraints [Articolo su rivista]
Sanudo Olmedo, Ignacio; Cavicchioli, Roberto; Capodieci, Nicola; Valente, Paolo; Bertogna, Marko
abstract

In the embedded systems domain, hypervisors are increasingly being adopted to guarantee timing isolation and appropriate hardware resource sharing among different software components. However, managing concurrent and parallel requests to shared hardware resources in a predictable way still represents an open issue. We argue that hypervisors can be an effective means to achieve an efficient and predictable arbitration of competing requests to shared devices in order to satisfy real-time requirements. As a representative example, we consider the case for mass storage (I/O) devices like Hard Disk Drives (HDD) and Solid State Disks (SSD), whose access times are orders of magnitude higher than those of central memory and CPU caches, therefore having a greater impact on overall task delays. We provide a comprehensive and up-to-date survey of the literature on I/O management within virtualized environments, focusing on software solutions proposed in the open source community, and discussing their main limitations in terms of realtime performance. Then, we discuss how the research in this subject may evolve in the future, highlighting the importance of techniques that are focused on scheduling not uniquely the processing bandwidth, but also the access to other important shared resources, like I/O devices.

2018 - Analytical Characterization of End-to-End Communication Delays With Logical Execution Time [Articolo su rivista]
Martinez, J.; Sanudo, I.; Bertogna, M.
abstract