A Clustering Algorithm for Communication-Aware Scheduling of Task Graphs on Multi-Core Reconfigurable Systems

Yoosefi, Amin; Naji, Hamid Reza

doi:10.1109/tpds.2017.2703123

Cited by 20 publications

(12 citation statements)

References 24 publications

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“…The studies [22], [23] present two scheduling solutions to effectively reconfigure and assign the tasks to cores while decreasing the global makespan under adaptive multi-core architectures. A hardware implementation of a dynamic communication-aware scheduler is proposed in [24]. This solution exploits the reconfigurable dominant sequence clustering algorithm to dynamically assign tasks to reconfigurable cores.…”

Section: Nomenclature Mmentioning

confidence: 99%

“…However PQ is also difficult to solve since its constraint (24) is non-linear as it contains the product of two variables β i and z i . So is (26) because of X sikp z i .…”

Section: ) Execution Modelmentioning

confidence: 99%

“…Stage 2: In this stage, we linearize (24) and (26) by using a linearization technique. Typically, the linearization [26] is based on the principle that each product is replaced by a new variable and then a set of new linear constraints is added to guarantee the equality between a new variable and the product.…”

Section: ) Execution Modelmentioning

confidence: 99%

“…In a similar way, we linearize (24). We replace X sikp z i by x i and we add the corresponding constraints:…”

Section: ) Execution Modelmentioning

confidence: 99%

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Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

et al. 2020

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This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous multicore platforms. Each processor is composed of a set of multi-speed cores with limited energy resources. A reconfigurable system is sensible to unpredictable reconfiguration events from related environment, such as the activation, removal or update of tasks. The problem is to handle feasible reconfiguration scenarios under energy constraints. Since any task can finish execution before achieving its worst-case execution time (WCET), the idea is to distribute this execution on different processor cores for meeting related deadlines and reducing energy consumption. The methodology consists in using lower processor speeds first to consume less energy. If the system is still non-feasible after reconfiguration, then we adjust the task periods as a flexible solution or migrate some of them to the least loaded processors. Accordingly, an integer linear program (ILP) is formulated to encode the execution model that assigns tasks to different cores with optimal energy consumption, thereby realizing energy-efficient computing/green computing. The potency and effectiveness of the proposed approach are rated through simulation studies. By measuring the energy consumption cost, our solution offers better than 11% of gain than recently published methods and improves by 85% the overall number of adjusted periods.

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Section: Nomenclature Mmentioning

confidence: 99%

“…However PQ is also difficult to solve since its constraint (24) is non-linear as it contains the product of two variables β i and z i . So is (26) because of X sikp z i .…”

Section: ) Execution Modelmentioning

confidence: 99%

Section: ) Execution Modelmentioning

confidence: 99%

“…In a similar way, we linearize (24). We replace X sikp z i by x i and we add the corresponding constraints:…”

Section: ) Execution Modelmentioning

confidence: 99%

See 2 more Smart Citations

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

et al. 2020

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“…Although its time complexity is theoretical exponential, much optimizations [25], [26] can be conducted to accelerate its computation speed. On the contrary, heuristic-based scheduling policies, including list-based scheduling [29]- [31], clustering-based scheduling [9], [32], [33], and duplication-based scheduling [10], have lower time complexity but the quality of solutions cannot be guaranteed. As the most widely used scheduling policy, list-based scheduling first determines the priority of each task and assigns tasks according to descendant priorities to their appropriate processors.…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Communication-Aware Task Scheduling Approach for Acyclic and Cyclic Applications on MPSoCs

Huang

Jiang

et al. 2019

IEEE Access

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Fine-grained task models can exploit parallelism to achieve high performance for multiprocessor system-on-chip (MPSoC). However, fine-grained models face the issues of high-communication overhead and difficult scheduling decisions, and the two challenges are interdependent. To address the issues, this paper gives a full analysis of the fine-grained communication optimization technique and communication pipeline, from both time and topology perspectives, and proposes a static fine-grained communication-aware task scheduling (FCATS) approach, which integrates scheduling with communication pipeline for acyclic and cyclic applications based on the fine-grained Simulink model. The approach contains search-based scheduling with high-quality solutions utilizing genetic algorithm-integer linear programming (GA-ILP) and hybrid GA-heuristic scheduling with short solving time to meet different demands for users. The experimental results with both synthetic and real-life benchmarks on the 4/8/16-CPU platform demonstrate the efficiency of the approach on performance improvements compared to previous works.

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Algorithms for reducing reconfiguration overheads using prefetch, reuse, and optimal mapping of tasks

Hariharan

Kannan

2018

Concurrency and Computation

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Summary Field Programmable Gate Arrays (FPGAs) are preferred in the modern embedded system to accelerate the performance of the entire system. However, the FPGAs are liable to suffer from reconfiguration overheads. These overheads are mainly because of the configuration data being fetched from the off‐chip memory at run‐time and due to the improper management of tasks during execution. To reduce these overheads, two algorithms are proposed. Both the algorithms focus on the prefetch heuristics, reuse technique, and an optimal mapping of tasks over the available memories. However, in terms of reusing technique, algorithm‐1 uses least recently used (LRU) policy and algorithm‐2 uses the optimal replacement policy (considering the vitality of the reconfigurable units (RUs)). Simulation results are obtained for both the algorithms. It is evident from the result that most of the reconfiguration overheads are eliminated when the applications are managed and executed based on the proposed algorithms. Also, the two algorithm results are compared and analyzed. For this purpose, the experiments included smaller and larger task graphs. In the case of smaller task graphs, algorithm‐2 outperforms algorithm‐1 in reducing reconfiguration overheads. In larger task graphs, algorithm‐1 produces better results compared to algorithm‐2.

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A Clustering Algorithm for Communication-Aware Scheduling of Task Graphs on Multi-Core Reconfigurable Systems

Cited by 20 publications

References 24 publications

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

Fine-Grained Communication-Aware Task Scheduling Approach for Acyclic and Cyclic Applications on MPSoCs

Algorithms for reducing reconfiguration overheads using prefetch, reuse, and optimal mapping of tasks

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