DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING

Authors

  • Shikai Wang Electrical and Computer Engineering, New York University, NY, USA Author
  • Haotian Zheng Electrical and Computer Engineering, New York University, NY, USA Author
  • Xin Wen Applied Data Science, University of Southern California, CA, USA Author
  • Fu Shang Data Science, New York University, NY, USA Author

DOI:

https://doi.org/10.60087/jklst.v3.n3.p108-126

Keywords:

Distributed Computing, Deep Learning Acceleration, High-Performance Systems, Communication-Efficient Algorithms

Abstract

This paper comprehensively analyzes distributed high-performance computing methods for accelerating deep learning training. We explore the evolution of distributed computing architectures, including data parallelism, model parallelism, and pipeline parallelism, and their hybrid implementations. The study delves into optimization techniques crucial for large-scale training, such as distributed optimization algorithms, gradient compression, and adaptive learning rate methods. We investigate communication-efficient algorithms, including Ring All Reduce variants and decentralized training approaches, which address the scalability challenges in distributed systems. The research examines hardware acceleration and specialized systems, focusing on GPU clusters, custom AI accelerators, high-performance interconnects, and distributed storage systems optimized for deep learning workloads. Finally, we discuss this field's challenges and future directions, including scalability-efficiency trade-offs, fault tolerance, energy efficiency in large-scale training, and emerging trends like federated learning and neuromorphic computing. Our findings highlight the synergy between advanced algorithms, specialized hardware, and optimized system designs in pushing the boundaries of large-scale deep learning, paving the way for future breakthroughs in artificial intelligence.

 

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Published

25-09-2024

How to Cite

Wang , S., Zheng , H., Wen , X. ., & Fu , S. (2024). DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 108-126. https://doi.org/10.60087/jklst.v3.n3.p108-126