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Conference Paper
FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation
- Authors
- Taeyoon Kim, ChanHo Park, Mansur Mukimbekov, Heelim Hong, Minseok Kim, Ze Jin, Changdae Kim, Ji-Yong Shin, Myeongjae Jeon
- Issue Date
- 2024-08
- Citation
- International Conference on Very Large DataBases (VLDB) 2024, pp.863-876
- Language
- English
- Type
- Conference Paper
- Abstract
- Data augmentation enhances the accuracy of DL models by diversifying training samples through a sequence of data transformations. While recent advancements in data augmentation have demonstrated remarkable efficacy, they often rely on computationally expensive and dynamic algorithms. Unfortunately, current system optimizations, primarily designed to leverage CPUs, cannot effectively support these methods due to costs and limited resource availability.
- KSP Keywords
- CPU-GPU, Data Augmentation, Data Preprocessing, Dynamic algorithm, Resource availability, Training samples, computationally expensive, machine Learning
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
