A study on optimally co-scheduling jobs of different lengths on chip multiprocessors

Tian, Kai; Jiang, Yunlian; Shen, Xipeng

doi:10.1145/1531743.1531752

Cited by 31 publications

(34 citation statements)

References 22 publications

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“…As mentioned earlier, there have been some studies in optimal co-scheduling for minimum cost [10,11,24]. We have mentioned the connections and differences between that co-scheduling problem and our makespan minimization problem throughout this paper.…”

Section: Comparison With Cost Minimizationmentioning

confidence: 92%

“…Overall, the previous work on cost minimization [10,11,24] has given some insights to this work. But because of the different goals of the two problems, this systematic exploration is imperative for achieving a good understanding of the makespan minimization problem.…”

Section: Comparison With Cost Minimizationmentioning

confidence: 99%

“…Although A*-search has been used for cost minimization problems [24], some substantial changes are necessary for applying it to makespan minimization. Specifically, we need redefine the structure of the search tree and the cost estimation function f (v).…”

Section: Application To Minimize Makespan For Job Co-schedulingmentioning

confidence: 99%

“…We do not claim the use of it for job co-scheduling in the general settings as a contribution of this work. In fact, previous work [24] has used it for approximating optimal schedules for cost minimization. However, the key in applying A* is in the formulation of the search problem and the design of the approximation functions f (v) used in every search-tree node.…”

Section: Comparison With Cost Minimizationmentioning

confidence: 99%

“…A*-search has been applied for job co-scheduling [24], but not for makespan minimization. Our description focuses on the issues specific to makespan minimization, including the formulation of the search process, the design of the heuristic function, and the empirical exploration of the tradeoff between the scheduling overhead and quality.…”

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confidence: 99%

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Optimal Co-Scheduling to Minimize Makespan on Chip Multiprocessors

Tian

Jiang

Shen

et al. 2013

Job Scheduling Strategies for Parallel Processing

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Abstract. On-chip resource sharing among sibling cores causes resource contention on Chip Multiprocessors (CMP), considerably degrading program performance and system fairness. Job co-scheduling attempts to alleviate the problem by assigning jobs to cores intelligently. Despite many heuristics-based empirical explorations, studies on optimal co-scheduling and its inherent complexity start only recently, and all have concentrated on the minimization of total performance degradations. There is another important criterion for scheduling, makespan, which determines the finish time of a job set. Its importance for job co-scheduling on CMP is increasingly recognized, especially with the rise of CMP-based compute cloud, data centers, and server farms. However, optimal coscheduling for makespan minimization still remains unexplored. This work compares makespan minimization problem with previously studied cost minimization (or degradation minimization) problem, revealing these connections as well as significant differences. It uncovers the computational complexity of the makespan minimization problem, and proposes a series of algorithms to either compute or approximate the optimal schedules. It proves that the problem is NP-complete in a general setting, but for a special case (dual-core without job migrations), the problem is solvable in O(n 2.5 · log n) time (n is the number of jobs). In addition, this paper presents a series of algorithms to compute or approximate the optimal schedules in the general setting. Experiments on both real and synthetic problems verify the optimality of the optimal co-scheduling algorithms, and demonstrate the reasonable accuracy and scalability of the approximation algorithms. The findings may advance the current understanding of optimal co-scheduling, facilitate the evaluation of real co-scheduling systems, and provide insights for the development of practical co-scheduling algorithms.

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Section: Comparison With Cost Minimizationmentioning

confidence: 92%

Section: Comparison With Cost Minimizationmentioning

confidence: 99%

Section: Application To Minimize Makespan For Job Co-schedulingmentioning

confidence: 99%

Section: Comparison With Cost Minimizationmentioning

confidence: 99%

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confidence: 99%

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