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Distributed deep learning framework tools should aim at high efficiency of training and inference of distributed exascale deep learning algorithms. There are three major challenges in this endeavor: scalability, adaptivity and efficiency. Any future framework will need to be adaptively utilized for a variety of heterogeneous hardware and network environments and will thus be required to be capable of scaling from single compute node up to large clusters. Further, it should be efficiently integrated into popular frameworks such as TensorFlow, PyTorch, etc. This paper proposes a dynamically hybrid (hierarchy) distribution structure for distributed deep learning, taking advantage of flexible synchronization on both centralized and decentralized architectures, implementing multi-level fine-grain parallelism on distributed platforms. It is scalable as the number of compute nodes increases, and can also adapt to various compute abilities, memory structures and communication costs.
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This content will become publicly available on June 7, 2026
Deep RC: A Scalable Data Engineering and Deep Learning Pipeline
Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep learning. Even while several large-scale solutions offer distributed execution environments, open-source alternatives that integrate scalable runtime tools, deep learning and data frameworks on high-performance computing platforms remain crucial for accessibility and flexibility. In this paper, we introduce Deep Radical-Cylon(RC), a heterogeneous runtime system that combines data engineering, deep learning frameworks, and workflow engines across several HPC environments, including cloud and supercomputing infrastructures. Deep RC supports heterogeneous systems with accelerators, allows the usage of communication libraries like MPI, GLOO and NCCL across multi-node setups, and facilitates parallel and distributed deep learning pipelines by utilizing Radical Pilot as a task execution framework. By attaining an end-to-end pipeline including preprocessing, model training, and postprocessing with 11 neural forecasting models (PyTorch) and hydrology models (TensorFlow) under identical resource conditions, the system reduces 3.28 and 75.9 seconds, respectively. The design of Deep RC guarantees the smooth integration of scalable data frameworks, such as Cylon, with deep learning processes, exhibiting strong performance on cloud platforms and scientific HPC systems. By offering a flexible, high-performance solution for resource-intensive applications, this method closes the gap between data preprocessing, model training, and postprocessing.
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- Award ID(s):
- 2411009
- PAR ID:
- 10634837
- Publisher / Repository:
- 28th edition of the workshop on Job Scheduling Strategies for Parallel Processing. JSSPP 2025 https://jsspp.org/
- Date Published:
- Format(s):
- Medium: X
- Location:
- Milan, Italy
- Sponsoring Org:
- National Science Foundation
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