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Automated prioritizing heuristics for parallel task graph scheduling in heterogeneous computing.

ABSTRACT: High-performance computing (HPC) relies increasingly on heterogeneous hardware and especially on the combination of central and graphical processing units. The task-based method has demonstrated promising potential for parallelizing applications on such computing nodes. With this approach, the scheduling strategy becomes a critical layer that describes where and when the ready-tasks should be executed among the processing units. In this study, we describe a heuristic-based approach that assigns priorities to each task type. We rely on a fitness score for each task/worker combination for generating priorities and use these for configuring the Heteroprio scheduler automatically within the StarPU runtime system. We evaluate our method's theoretical performance on emulated executions and its real-case performance on multiple different HPC applications. We show that our approach is usually equivalent or faster than expert-defined priorities.

SUBMITTER: Flint C

PROVIDER: S-EPMC9575933 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Automated prioritizing heuristics for parallel task graph scheduling in heterogeneous computing.

Flint Clément C Paillat Ludovic L Bramas Bérenger B

PeerJ. Computer science 20220916

High-performance computing (HPC) relies increasingly on heterogeneous hardware and especially on the combination of central and graphical processing units. The task-based method has demonstrated promising potential for parallelizing applications on such computing nodes. With this approach, the scheduling strategy becomes a critical layer that describes where and when the ready-tasks should be executed among the processing units. In this study, we describe a heuristic-based approach that assigns ...[more]

PMID: 36262161

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Project description:BackgroundDrug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency.ResultsWe find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied.ConclusionKnowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.

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Dataset Information

Automated prioritizing heuristics for parallel task graph scheduling in heterogeneous computing.

Publications

Automated prioritizing heuristics for parallel task graph scheduling in heterogeneous computing.

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