Spinner: Enhancing HPC Experimentation with a Streamlined Parameter Sweep Tool

We introduce Spinner, an open-source, parameterized sweep benchmark tool for high-performance computing (HPC). Spinner employs a YAML file format to facilitate the execution of applications, data collection, and metadata versioning and sharing. This setup simplifies version control and sharing of experimental configurations. By including dataframes that describe experiments and raw data, it allows readers and reviewers to gain a clearer understanding than summary statistics alone. Spinner's design is non-intrusive and efficient, executing user-supplied commands and dynamically replacing parameters in the command template. It also features the capability to rerun failed experiments, which is particularly useful for testing unstable applications or identifying unstable parameter combinations. We have demonstrated Spinner's functionality with minimal configuration by using it in tests with LAMMPS, GROMACS, Task Bench, XSBench, and RSBench on two different clusters. These tests measured wall time and captured application outputs, underscoring Spinner's practicality and adaptability.

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Publication Details

Published
2024-11-13
DOI
https://doi.org/10.1109/sbac-padw64858.2024.00013
Citations
5
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
1.74

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Spinner: Enhancing HPC Experimentation with a Streamlined Parameter Sweep Tool

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Spinner: Enhancing HPC Experimentation with a Streamlined Parameter Sweep Tool

Gustavo Leite, Rodrigo Ceccato, Hervé Yviquel, Jose M. Monsalve Diaz, Jhonatan Cléto, Sandro Rigo
article en
5 citations

Abstract

We introduce Spinner, an open-source, parameterized sweep benchmark tool for high-performance computing (HPC). Spinner employs a YAML file format to facilitate the execution of applications, data collection, and metadata versioning and sharing. This setup simplifies version control and sharing of experimental configurations. By including dataframes that describe experiments and raw data, it allows readers and reviewers to gain a clearer understanding than summary statistics alone. Spinner's design is non-intrusive and efficient, executing user-supplied commands and dynamically replacing parameters in the command template. It also features the capability to rerun failed experiments, which is particularly useful for testing unstable applications or identifying unstable parameter combinations. We have demonstrated Spinner's functionality with minimal configuration by using it in tests with LAMMPS, GROMACS, Task Bench, XSBench, and RSBench on two different clusters. These tests measured wall time and captured application outputs, underscoring Spinner's practicality and adaptability.

Universidade Estadual de Campinas (UNICAMP) (BR)
Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Petrobras, Universidade Estadual de Campinas
Openalex Percentile: Top 14%
Advanced Data Storage Technologies
1.74
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