Dynamic Behavior Specification and Dynamic Mapping for Real-Time Embedded Systems

Jung, Hyun Ae; Lee, Chanhee; Kang, Shin-haeng; Kim, Sung‐Chan; Oh, Heekuck; Ha, Soonhoi

doi:10.1145/2584658

Cited by 23 publications

(13 citation statements)

References 36 publications

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“…One approach is to support multiple modes of operation in which each mode of operation is specified by an SDF graph. Finite State Machine (FSM)-based scenario-aware dataflow (SADF) [33], Mode Transition Machine (MTM) SADF [34], and mode-aware dataflow (MADF) [35] are some examples of this approach. As a common subset of those approaches, we assume an extended SDF model, denoted as multi-mode SDF (MMSDF), and explain how the proposed clustering technique can be applied.…”

Section: Supporting Multi-mode Sdf Modelmentioning

confidence: 99%

Parallel Scheduling of Multiple SDF Graphs Onto Heterogeneous Processors

et al. 2021

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Parallel scheduling of multiple real-time applications onto heterogeneous processors is needed in the emerging embedded systems such as self-driving cars, smart cameras, and smartphones. Assuming that an embedded application is specified as a synchronous dataflow (SDF) graph or its extension, we propose a novel parallel scheduling methodology based on an evolutionary algorithm where the mapping of tasks onto processors is evolved to optimize a given objective function in an iterative fashion. In each iteration, we use an existing worst-case response time (WCRT) analysis tool to check if all applications satisfy their real-time requirements by translating each SDF graph into a directed acyclic graph (DAG) that is assumed in the WCRT analysis tool. Since the WCRT analysis must be performed in each iteration of evolution, we propose a clustering technique to reduce drastically the analysis time that depends on the number of nodes and their dependency. We formally prove that the proposed clustering technique does not change the estimated WCRT of each application. The effectiveness of the proposed scheduling methodology with the clustering technique is verified with extensive experiments using real-life benchmarks, randomly generated graphs, and the comparison with the existing technique.

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Section: Supporting Multi-mode Sdf Modelmentioning

confidence: 99%

Parallel Scheduling of Multiple SDF Graphs Onto Heterogeneous Processors

et al. 2021

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“…Several resource allocation approaches have been proposed while following different policies. Most of these approaches map communicating tasks of each application close to each other such that communication overhead and power are reduced [2], [7], [8], [9], [11], [21], [23], [31], [34]. Some of these approaches also reduce computation power of the cores by employing voltage/frequency scaling [9].…”

Section: Related Workmentioning

confidence: 99%

Bubble Budgeting: Throughput Optimization for Dynamic Workloads by Exploiting Dark Cores in Many Core Systems

Wang

Singh

et al. 2018

IEEE Trans. Comput.

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Abstract-All the cores of a many-core chip cannot be active at the same time, due to reasons like low CPU utilization in server systems and limited power budget in dark silicon era. These free cores (referred to as bubbles) can be placed near active cores for heat dissipation so that the active cores can run at a higher frequency level, boosting the performance of applications that run on active cores. Budgeting inactive cores (bubbles) to applications to boost performance has the following three challenges. First, the number of bubbles varies due to open workloads. Second, communication distance increases when a bubble is inserted between two communicating tasks (a task is a thread or process of a parallel application), leading to performance degradation. Third, budgeting too many bubbles as coolers to running applications leads to insufficient cores for future applications. In order to address these challenges, in this paper, a bubble budgeting scheme is proposed to budget free cores to each application so as to optimize the throughput of the whole system. Throughput of the system depends on the execution time of each application and the waiting time incurred for newly arrived applications. Essentially, the proposed algorithm determines the number and locations of bubbles to optimize the performance and waiting time of each application, followed by tasks of each application being mapped to a core region. A Rollout algorithm is used to budget power to the cores as the last step. Experiments show that our approach achieves 50% higher throughput when compared to state-of-the-art thermal-aware runtime task mapping approaches. The runtime overhead of the proposed algorithm is in the order of 1M cycles, making it an efficient runtime task management method for large-scale many-core systems.

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“…The trend for the hybrid resource allocation was introduced earlier ]. Since then, due to its potential, significant advances have taken place for embedded systems [Weichslgartner et al 2014;Javaid et al 2014;Jung et al 2014;Quan and Pimentel 2015;Dziurzanski et al 2015;Singh et al 2016b]. It has also shown its potential for managing data center resources [Singh et al 2015b;.…”

Section: Hybrid Resource Allocationmentioning

confidence: 99%

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

et al. 2017

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Multi/Many-core systems are envisioned to satisfy the ever increasing performance requirements of complex applications in various domains such as embedded and high performance computing (HPC). Such systems need to cater for increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies in order to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed over the last two decades and highlights the emerging trends for multi/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles such as market and biological concepts to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided.

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Dynamic Behavior Specification and Dynamic Mapping for Real-Time Embedded Systems

Cited by 23 publications

References 36 publications

Parallel Scheduling of Multiple SDF Graphs Onto Heterogeneous Processors

Parallel Scheduling of Multiple SDF Graphs Onto Heterogeneous Processors

Bubble Budgeting: Throughput Optimization for Dynamic Workloads by Exploiting Dark Cores in Many Core Systems

A Survey and Comparative Study of Hard and Soft Real-Time Dynamic Resource Allocation Strategies for Multi-/Many-Core Systems

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