Independent and Divisible Tasks Scheduling on Heterogeneous Star-shaped Platforms with Limited Memory

Beaumont, Olivier; Legrand, Arnaud; Marchal, Loris; Robert, Yves

doi:10.1109/empdp.2005.25

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…This period may be huge, requiring the nodes to have unreasonably large buffers to ensure uninterrupted steady-state behavior. The problem of buffer size has already been pointed out in [21] and [31], where it is shown that no finite amount of buffer space is sufficient for every tree. It is also known that finding the optimal throughput when buffer sizes are bounded is a strongly NP-hard problem even in very simple situations [31].…”

Section: Demand-driven and Decentralized Heuristicsmentioning

confidence: 99%

Centralized versus Distributed Schedulers for Bag-of-Tasks Applications

Beaumont

Carter

Ferrante

et al. 2008

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper, we consider the problem of scheduling applications to ensure fair and efficient execution on a distributed network of processors. We limit our study to the case where communication is restricted to a tree embedded in the network, and the applications consist of a large number of independent tasks (Bags of Tasks) that originate at the tree's root. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary for different applications. The goal of scheduling is to maximize the throughput of each application while ensuring a fair sharing of resources between applications. We can find the optimal asymptotic rates by solving a linear programming problem that expresses all necessary problem constraints, and we show how to construct a periodic schedule from any linear program solution. For single-level trees, the solution is characterized by processing tasks with larger communication-to-computation ratios at children with larger bandwidths. For multilevel trees, this approach requires global knowledge of all application and platform parameters. For large-scale platforms, such global coordination by a centralized scheduler may be unrealistic. Thus, we also investigate decentralized schedulers that use only local information at each participating resource. We assess their performance via simulation and compare to an optimal centralized solution obtained via linear programming. The best of our decentralized heuristics achieves the same performance on about 2/3 of our test cases but is far worse in a few cases. Although our results are based on simple assumptions and do not explore all parameters (such as the maximum number of tasks that can be held on a node), they provide insight into the important question of fairly and optimally scheduling heterogeneous applications on heterogeneous grids.

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Section: Demand-driven and Decentralized Heuristicsmentioning

confidence: 99%

Centralized versus Distributed Schedulers for Bag-of-Tasks Applications

Beaumont

Carter

Ferrante

et al. 2008

IEEE Trans. Parallel Distrib. Syst.

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“…This period may be huge, requiring the nodes to have unreasonably large buffers to ensure uninterrupted steady-state behavior. The problem of buffer size has already been pointed out in [12,7], where it is shown that no finite amount of buffer space is sufficient for every tree. It is also known that finding the optimal throughput when buffer sizes are bounded is a strongly NP-hard problem even in very simple situations [7].…”

Section: Demand-driven and Decentralized Heuristicsmentioning

confidence: 99%

Centralized versus distributed schedulers for multiple bag-of-task applications

Beaumont¹,

Carter²,

Ferrante³

et al. 2006

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

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“…We choose not to include this constraint in our basic set of constraints, as this buffer size limitation only applies to the fluid model. Indeed, we have earlier proved that limiting the buffer size for independent tasks scheduling leads to NP-complete problems [17].…”

Section: Bounding Buffer Sizesmentioning

confidence: 99%

Scheduling Concurrent Bag-of-Tasks Applications on Heterogeneous Platforms

Benoît

Marchal

Pineau

et al. 2010

IEEE Trans. Comput.

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Abstract-Scheduling problems are already difficult on traditional parallel machines, and they become extremely challenging on heterogeneous clusters. In this paper, we deal with the problem of scheduling multiple applications, made of collections of independent and identical tasks, on a heterogeneous master-worker platform. The applications are submitted online, which means that there is no a priori (static) knowledge of the workload distribution at the beginning of the execution. The objective is to minimize the maximum stretch, i.e., the maximum ratio between the actual time an application has spent in the system and the time this application would have spent if executed alone. On the theoretical side, we design an optimal algorithm for the offline version of the problem (when all release dates and application characteristics are known beforehand). We also introduce a heuristic for the general case of online applications. On the practical side, we have conducted extensive simulations and MPI experiments, showing that we are able to deal with very large problem instances in a few seconds. Also, the solution that we compute totally outperforms classical heuristics from the literature, thereby fully assessing the usefulness of our approach.

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Independent and Divisible Tasks Scheduling on Heterogeneous Star-shaped Platforms with Limited Memory

Cited by 12 publications

References 18 publications

Centralized versus Distributed Schedulers for Bag-of-Tasks Applications

Centralized versus Distributed Schedulers for Bag-of-Tasks Applications

Centralized versus distributed schedulers for multiple bag-of-task applications

Scheduling Concurrent Bag-of-Tasks Applications on Heterogeneous Platforms

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