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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.12407 |
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| _version_ | 1866917030919143424 |
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| author | Luan, Frank Sifei Wang, Ron Yifeng Gu, Yile Mao, Ziming Lin, Charlotte Kamsetty, Amog Chen, Hao Su, Cheng Veeramani, Balaji Lee, Scott Cho, SangBin Zinzow, Clark Liang, Eric Stoica, Ion Wang, Stephanie |
| author_facet | Luan, Frank Sifei Wang, Ron Yifeng Gu, Yile Mao, Ziming Lin, Charlotte Kamsetty, Amog Chen, Hao Su, Cheng Veeramani, Balaji Lee, Scott Cho, SangBin Zinzow, Clark Liang, Eric Stoica, Ion Wang, Stephanie |
| contents | While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Distributed data processing systems based on the batch or stream processing models assume homogeneous resource requirements. They excel at CPU-based computation but either under-utilize heterogeneous resources or impose high overheads on failure and reconfiguration.
We introduce the streaming batch model, a hybrid of batch and streaming that enables efficient and fault-tolerant heterogeneous execution. The key idea is to use partitions as the unit of execution to achieve elasticity, but to allow partitions to be dynamically created and streamed between heterogeneous operators for memory-efficient pipelining. We present Ray Data, a streaming batch system that improves throughput on heterogeneous batch inference pipelines by 2.5-12$\times$ compared to traditional batch and stream processing systems. By leveraging heterogeneous clusters, Ray Data improves training throughput for multimodal models such as Stable Diffusion by 31% compared to single-node ML data loaders. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12407 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous Execution Luan, Frank Sifei Wang, Ron Yifeng Gu, Yile Mao, Ziming Lin, Charlotte Kamsetty, Amog Chen, Hao Su, Cheng Veeramani, Balaji Lee, Scott Cho, SangBin Zinzow, Clark Liang, Eric Stoica, Ion Wang, Stephanie Distributed, Parallel, and Cluster Computing Machine Learning While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Distributed data processing systems based on the batch or stream processing models assume homogeneous resource requirements. They excel at CPU-based computation but either under-utilize heterogeneous resources or impose high overheads on failure and reconfiguration. We introduce the streaming batch model, a hybrid of batch and streaming that enables efficient and fault-tolerant heterogeneous execution. The key idea is to use partitions as the unit of execution to achieve elasticity, but to allow partitions to be dynamically created and streamed between heterogeneous operators for memory-efficient pipelining. We present Ray Data, a streaming batch system that improves throughput on heterogeneous batch inference pipelines by 2.5-12$\times$ compared to traditional batch and stream processing systems. By leveraging heterogeneous clusters, Ray Data improves training throughput for multimodal models such as Stable Diffusion by 31% compared to single-node ML data loaders. |
| title | The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous Execution |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2501.12407 |