Summary of Enriching the Machine Learning Workloads in Bigbench, by Matthias Polag et al.
Enriching the Machine Learning Workloads in BigBench
by Matthias Polag, Todor Ivanov, Timo Eichhorn
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed BigBench V2 framework enhances the original TPCx-BB benchmark by introducing three new workloads that evaluate various machine learning algorithms. The new workloads leverage multiple algorithms from popular libraries such as MLlib, SystemML, Scikit-learn, and Pandas, providing a comprehensive assessment of these implementations. This extension aims to standardize application benchmarks for evaluating Machine Learning, Deep Learning, and Artificial Intelligence technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The BigBench V2 benchmark is designed to help evaluate machine learning algorithms by testing their performance across different libraries and frameworks. The new workloads added to the benchmark demonstrate the importance of using standardized evaluation tools in the development and comparison of machine learning models. By providing a comprehensive assessment of various algorithms, BigBench V2 helps ensure that the best-performing models are selected for real-world applications. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
