A Survey of Techniques for Reducing Interference in Real-Time Applications on Multicore Platforms

Lugo, Tamara; Lozano, Santiago; Fernández, Julia Martínez; Carretero, Jesús

doi:10.1109/access.2022.3151891

Cited by 21 publications

(5 citation statements)

References 201 publications

(322 reference statements)

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“…In recent years, there has been a surge in research focused on reducing interference in multicore systems. A notable contribution to this field is the recent survey by Lugo et al [12], which highlights key advancements in interference reduction techniques within multicore systems over the past five years. This survey provides a comprehensive overview of the latest research efforts and their contributions to mitigating interference in multicore environments.…”

Section: Related Workmentioning

confidence: 99%

Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention

Ortiz,

Guasque,

Balbastre

et al. 2024

Applied Sciences

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In the scheduling of hard real-time systems on multicore platforms, significant unpredictability arises from interference caused by shared hardware resources. The objective of this paper is to offer a schedulability analysis for such systems by assuming a general model that introduces interference as a time parameter for each task. The analysis assumes constrained deadlines and is provided for fixed priorities. It is based on worst-case response time analysis, which exists in the literature for monocore systems. We demonstrate that the worst-case response time is an upper bound, and we evaluate our proposal with synthetic loads and execution on a real platform.

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Section: Related Workmentioning

confidence: 99%

Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention

Ortiz,

Guasque,

Balbastre

et al. 2024

Applied Sciences

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“…This work is focused in partitioned hard real-time systems that take into account interference due to contention. The most recent survey about interference and mitigation of its effect in the scheduling can be found in [5] (until 2021).…”

Section: Related Workmentioning

confidence: 99%

Optimized Scheduling of Periodic Hard Real-Time Multicore Systems

Aceituno¹,

Guasque²,

Balbastre³

et al. 2023

IEEE Access

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Multicore systems were developed to provide a substantial performance increase over monocore systems. But shared hardware resources are a problem as they add unpredictable delays that affect the schedulability of multicore hard real-time systems. In recent years much effort has been put into modelling interference and proposing scheduling techniques to mitigate its negative effect. Using one of these models, we propose a scheduling technique, based on Integer Linear Programming (ILP) that, in combination with a task to core allocator, not only achieves a feasible schedule but also reduces the interference produced by shared hardware resources in the context of hard real-time multicore systems. The evaluation shows how both the interference is reduced (≈ 83.47%) and the schedulability is increased (≈ 12.25%) compared to traditional heuristics.INDEX TERMS integer linear programming, optimization, partitioned systems, real-time systems, static scheduling I. INTRODUCTION

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“…Interference on shared memory resources has been previously observed in both multicore CPUs [10]- [13], within the GPU computing clusters [14], [15] and between CPU and GPU in integrated SoCs [1], [3], [16]. Focusing on this latter topic, several approaches have been proposed in order to mitigate the effect of memory interference on both performance and predictability.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Masola,

Capodieci,

Rouxel

et al. 2023

2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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Nowadays, heterogeneous embedded platforms are extensively used in various low-latency applications, including the automotive industry, real-time IoT systems, and automated factories. These platforms utilize specific components, such as CPUs, GPUs, and neural network accelerators for efficient task processing and to solve specific problems with a lower power consumption compared to more traditional systems. However, since these accelerators share resources such as the global memory, it is crucial to understand how workloads behave under high computational loads to determine how parallel computational engines on modern platforms can interfere and adversely affect the system's predictability and performance. One area that remains unclear is the interference effect on shared memory resources between the CPU and GPU: more specifically, the latency degradation experienced by GPU kernels when memory-intensive CPU applications run concurrently. In this work, we first analyze the metrics that characterize the behavior of different kernels under various board conditions caused by CPU memory-intensive workloads on a Nvidia Jetson Xavier. Then, we exploit various machine learning methodologies aiming to estimate the latency degradation of kernels based on their metrics. As a result of this, we are able to identify the metrics that could potentially have the most significant impact when predicting the kernels completion latency degradation.

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A Survey of Techniques for Reducing Interference in Real-Time Applications on Multicore Platforms

Cited by 21 publications

References 201 publications

Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention

Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention

Optimized Scheduling of Periodic Hard Real-Time Multicore Systems

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

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