Scheduling moldable tasks on homogeneous multi-cluster platforms with GPUs
This paper examines task scheduling in homogeneous multi-cluster platforms, equipped with Graphics Processing Units (GPUs), with the aim of minimizing the makespan. We assume that tasks can be parallelized across these platforms under the moldable model. Recognizing the NP-hard nature of the problem...
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| Vydáno v: | Computers & operations research Ročník 179; s. 107041 |
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| Jazyk: | angličtina |
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01.07.2025
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| ISSN: | 0305-0548 |
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| Abstract | This paper examines task scheduling in homogeneous multi-cluster platforms, equipped with Graphics Processing Units (GPUs), with the aim of minimizing the makespan. We assume that tasks can be parallelized across these platforms under the moldable model. Recognizing the NP-hard nature of the problem, our goal is to develop algorithms that provide approximation ratios. While existing research has established algorithms for single-cluster GPU environments, scaling these to multi-cluster platforms introduces new challenges, especially due to the restriction that tasks cannot use processors from different clusters. We propose an integer programming-based algorithm that achieves an approximation ratio of 32+ϵ, trading off runtime for an improved approximation ratio. Additionally, leveraging recent theoretical advancements, we have created a polynomial-time algorithm with an approximation ratio of 2+ϵ. Empirical computational experiments show that our algorithms surpass their counterparts in empirical approximation ratios. |
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| AbstractList | This paper examines task scheduling in homogeneous multi-cluster platforms, equipped with Graphics Processing Units (GPUs), with the aim of minimizing the makespan. We assume that tasks can be parallelized across these platforms under the moldable model. Recognizing the NP-hard nature of the problem, our goal is to develop algorithms that provide approximation ratios. While existing research has established algorithms for single-cluster GPU environments, scaling these to multi-cluster platforms introduces new challenges, especially due to the restriction that tasks cannot use processors from different clusters. We propose an integer programming-based algorithm that achieves an approximation ratio of 32+ϵ, trading off runtime for an improved approximation ratio. Additionally, leveraging recent theoretical advancements, we have created a polynomial-time algorithm with an approximation ratio of 2+ϵ. Empirical computational experiments show that our algorithms surpass their counterparts in empirical approximation ratios. |
| ArticleNumber | 107041 |
| Author | Zhang, Xiandong Zhang, Run Wu, Fangfang |
| Author_xml | – sequence: 1 givenname: Fangfang surname: Wu fullname: Wu, Fangfang email: ffwu@shmtu.edu.cn organization: School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China – sequence: 2 givenname: Run surname: Zhang fullname: Zhang, Run email: zhangr20@fudan.edu.cn organization: School of Management, Fudan University, Shanghai, 200433, China – sequence: 3 givenname: Xiandong orcidid: 0000-0003-1442-3859 surname: Zhang fullname: Zhang, Xiandong email: xiandongzhang@fudan.edu.cn organization: School of Management, Fudan University, Shanghai, 200433, China |
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| Keywords | Dual approximation algorithm Multi-cluster platform Moldable task scheduling Approximation algorithm |
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| SubjectTerms | Approximation algorithm Dual approximation algorithm Moldable task scheduling Multi-cluster platform |
| Title | Scheduling moldable tasks on homogeneous multi-cluster platforms with GPUs |
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