Thicket: Seeing the Performance Experiment Forest for the Individual Run Trees Article Swipe
Stephanie Brink
,
Michael McKinsey
,
David Boehme
,
Connor Scully-Allison
,
Ian Lumsden
,
W. Daryl Hawkins
,
Treece Burgess
,
Vanessa Lama
,
Jakob Lüttgau
,
Katherine E. Isaacs
,
Michela Taufer
,
Olga Pearce
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3588195.3592989
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3588195.3592989
Thicket is an open-source Python toolkit for Exploratory Data Analysis (EDA) of multi-run performance experiments. It enables an understanding of optimal performance configuration for large-scale application codes. Most performance tools focus on a single execution (e.g., single platform, single measurement tool, single scale). Thicket bridges the gap to convenient analysis in multi-dimensional, multi-scale, multi-architecture, and multi-tool performance datasets by providing an interface for interacting with the performance data. Thicket has a modular structure composed of three components. The first component is a data structure for multi-dimensional performance data, which is composed automatically on the portable basis of call trees, and accommodates any subset of dimensions present in the dataset. The second is the metadata, enabling distinction and sub-selection of dimensions in performance data. The third is a dimensionality reduction mechanism, enabling analysis such as computing aggregated statistics on a given data dimension. Extensible mechanisms are available for applying analyses (e.g., top-down on Intel CPUs), data science techniques (e.g., K-means clustering from scikit-learn), modeling performance (e.g., Extra-P), and interactive visualization. We demonstrate the power and flexibility of Thicket through two case studies, first with the open-source RAJA Performance Suite on CPU and GPU clusters and another with a large physics simulation run on both a traditional HPC cluster and an AWS Parallel Cluster instance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3588195.3592989
- https://dl.acm.org/doi/pdf/10.1145/3588195.3592989
- OA Status
- gold
- Cited By
- 15
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385623175
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385623175Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3588195.3592989Digital Object Identifier
- Title
-
Thicket: Seeing the Performance Experiment Forest for the Individual Run TreesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-07Full publication date if available
- Authors
-
Stephanie Brink, Michael McKinsey, David Boehme, Connor Scully-Allison, Ian Lumsden, W. Daryl Hawkins, Treece Burgess, Vanessa Lama, Jakob Lüttgau, Katherine E. Isaacs, Michela Taufer, Olga PearceList of authors in order
- Landing page
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https://doi.org/10.1145/3588195.3592989Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3588195.3592989Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3588195.3592989Direct OA link when available
- Concepts
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Computer science, Python (programming language), Cluster analysis, Modular design, Visualization, Interface (matter), Metadata, Dimensionality reduction, Data mining, Parallel computing, Operating system, Artificial intelligence, Maximum bubble pressure method, BubbleTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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