Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.10843 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916288859734016 |
|---|---|
| author | Polag, Matthias Ivanov, Todor Eichhorn, Timo |
| author_facet | Polag, Matthias Ivanov, Todor Eichhorn, Timo |
| contents | In the era of Big Data and the growing support for Machine Learning, Deep Learning and Artificial Intelligence algorithms in the current software systems, there is an urgent need of standardized application benchmarks that stress test and evaluate these new technologies. Relying on the standardized BigBench (TPCx-BB) benchmark, this work enriches the improved BigBench V2 with three new workloads and expands the coverage of machine learning algorithms. Our workloads utilize multiple algorithms and compare different implementations for the same algorithm across several popular libraries like MLlib, SystemML, Scikit-learn and Pandas, demonstrating the relevance and usability of our benchmark extension. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enriching the Machine Learning Workloads in BigBench Polag, Matthias Ivanov, Todor Eichhorn, Timo Machine Learning In the era of Big Data and the growing support for Machine Learning, Deep Learning and Artificial Intelligence algorithms in the current software systems, there is an urgent need of standardized application benchmarks that stress test and evaluate these new technologies. Relying on the standardized BigBench (TPCx-BB) benchmark, this work enriches the improved BigBench V2 with three new workloads and expands the coverage of machine learning algorithms. Our workloads utilize multiple algorithms and compare different implementations for the same algorithm across several popular libraries like MLlib, SystemML, Scikit-learn and Pandas, demonstrating the relevance and usability of our benchmark extension. |
| title | Enriching the Machine Learning Workloads in BigBench |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.10843 |