List Scheduling for Heterogeneous Computing Systems Introducing a Performance-Effective Definition for Critical Path

Dorostkar, Farzam; Mirzakuchaki, Sattar

doi:10.1109/iccke48569.2019.8964738

Cited by 3 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The list scheduling algorithm is divided into two main phases: (1) the task-selection phase, which selects the ready task with the highest priority, and (2) the processor selection phase, which selects a suitable processor such that the objective function is minimized. MOELS algorithm was proposed in [13] and four list scheduling algorithms were extended in [14]. Both [13,14] study workflow scheduling methods, and although both SDFG and workflow can be represented using the DAG model, SDFG needs to consider its periodicity.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems

Wang

2022

Algorithms

View full text Add to dashboard Cite

In order to accelerate the execution of streaming applications on multi-core systems, this article studies the scheduling problem of synchronous data flow graphs (SDFG) on homogeneous multi-core systems. To describe the data flow computation process, we propose the SDAG (Super DAG) computation model based on the DAG model combined with data-driven thoughts. Further, we analyze the current common SDFG scheduling algorithms and propose an improved SDFG scheduling algorithm, LSEFT (level-shortest-first-earliest-finish time). The LSEFT algorithm uses an inverse traversal algorithm to calculate the priority of tasks in the task-selection phase; the shortest-job-priority earliest-finish-time policy is used in the processor selection phase to replace the original long job priority policy. In the experimental part, we designed an SDFG random generator and generated 958 SDFGs with the help of the random generator as test cases to verify the scheduling algorithm. The experimental results show that our improved algorithm performs well for different numbers of processor cores, especially for 8 cores, where the speedup of our improved algorithm improves by 10.17% on average.

show abstract

Section: Related Workmentioning

confidence: 99%

“…MOELS algorithm was proposed in [13] and four list scheduling algorithms were extended in [14]. Both [13,14] study workflow scheduling methods, and although both SDFG and workflow can be represented using the DAG model, SDFG needs to consider its periodicity. Ref.…”

Section: Related Workmentioning

confidence: 99%

Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems

Wang

2022

Algorithms

View full text Add to dashboard Cite

show abstract

“…The common heuristic methods are list scheduling algorithm, clustering algorithm, and duplication-based task scheduling algorithm. List scheduling algorithms [13][14][15][16][17][18] are mainly divided into two steps to complete: (i) calculate the priority of each task and construct a task scheduling queue, without breaking the precedence relationships; (ii) take the top task from the queue and assign the optimal processor to it, until all the tasks are scheduled. And then, dynamic list scheduling algorithms are proposed.…”

Section: Related Workmentioning

confidence: 99%

Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

Liu

Chen

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

Distributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy-conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy-conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption.Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy-conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real-world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy-saving and makespan reducing.

show abstract

An Improved ACS Algorithm by CA for Task Scheduling in Heterogeneous Multiprocessing Environments

Liu

Ren

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

List Scheduling for Heterogeneous Computing Systems Introducing a Performance-Effective Definition for Critical Path

Cited by 3 publications

References 11 publications

Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems

Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems

Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

An Improved ACS Algorithm by CA for Task Scheduling in Heterogeneous Multiprocessing Environments

Contact Info

Product

Resources

About