Mapping Tree-Shaped Workflows on Memory-Heterogeneous Architectures

Kulagina, Svetlana; Meyerhenke, Henning; Benoît, Anne

doi:10.1007/978-3-031-31209-0_12

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Since the processors have identical computing speeds (and only memories of different size), the makespan after a merge can be computed by applying Equation (1). More precisely, we compute the difference Δ i between the makespans before and after the merge of node i.…”

Section: Decreasing the Number Of Subtreesmentioning

confidence: 99%

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Mapping tree‐shaped workflows on systems with different memory sizes and processor speeds

Kulagina

Meyerhenke

Benoît

2023

Concurrency and Computation

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Directed acyclic graphs are commonly used to model scientific workflows, by expressing dependencies between tasks, as well as the resource requirements of the workflow. As a special case, rooted directed trees occur in several applications, for instance in sparse matrix computations. Since typical workflows are modeled by large trees, it is crucial to schedule them efficiently, so that their execution time (or makespan) is minimized. Furthermore, it is usually beneficial to distribute the execution on several compute nodes, hence increasing the available memory, and allowing us to parallelize parts of the execution. To exploit the heterogeneity of modern clusters in this context, we investigate the partitioning and mapping of tree‐shaped workflows on two types of target architecture models: in AM1, each processor can have a different memory size, and in AM2, each processor can also have a different speed (in addition to a different memory size). We design a three‐step heuristic for AM1, which adapts and extends previous work for homogeneous clusters [Gou C, Benoit A, Marchal L. Partitioning tree‐shaped task graphs for distributed platforms with limited memory. IEEE Trans Parallel Dist Syst 2020; 31(7): 1533–1544]. The changes we propose concern the assignment to processors (accounting for the different memory sizes) and the availability of suitable processors when splitting or merging subtrees. For AM2, we extend the heuristic for AM1 with a two‐phase local search approach. Phase A is a swap‐based hill climber, while (the optional) Phase B is inspired by iterated local search. We evaluate our heuristics for AM1 and AM2 with extensive simulations, and we demonstrate that exploiting the heterogeneity in the cluster significantly reduces the makespan, compared to the state of the art for homogeneous processors.

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Section: Decreasing the Number Of Subtreesmentioning

confidence: 99%

“…graph representing the tasks and the edges their dependencies. One common form of such DAGs is a rooted directed tree, 1 which we consider in this paper. These tree-shaped workflows occur in a variety of applications, for example, in sparse matrix factorizations and computational physics.…”

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Mapping tree‐shaped workflows on systems with different memory sizes and processor speeds

Kulagina

Meyerhenke

Benoît

2023

Concurrency and Computation

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Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures

Bader

Lehmann

Groth

et al. 2022

2022 IEEE International Performance, Computing, and Communications Conference (IPCCC)

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Scientific workflows typically comprise a multitude of different processing steps which often are executed in parallel on different partitions of the input data. These executions, in turn, must be scheduled on the compute nodes of the computational infrastructure at hand. This assignment is complicated by the facts that (a) tasks typically have highly heterogeneous resource requirements and (b) in many infrastructures, compute nodes offer highly heterogeneous resources. In consequence, predictions of the runtime of a given task on a given node, as required by many scheduling algorithms, are often rather imprecise, which can lead to sub-optimal scheduling decisions.We propose Reshi, a method for recommending task-node assignments during workflow execution that can cope with heterogeneous tasks and heterogeneous nodes. Reshi approaches the problem as a regression task, where task-node pairs are modeled as feature vectors over the results of dedicated micro benchmarks and past task executions. Based on these features, Reshi trains a regression tree model to rank and recommend nodes for each ready-to-run task, which can be used as input to a scheduler. For our evaluation, we benchmarked 27 AWS machine types using three representative workflows. We compare Reshi's recommendations with three state-of-the-art schedulers. Our evaluation shows that Reshi outperforms HEFT by a mean makespan reduction of 7.18% and 18.01% assuming a mean task runtime prediction error of 15%.

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Mapping Tree-Shaped Workflows on Memory-Heterogeneous Architectures

Cited by 2 publications

References 16 publications

Mapping tree‐shaped workflows on systems with different memory sizes and processor speeds

Mapping tree‐shaped workflows on systems with different memory sizes and processor speeds

Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures

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