Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis (1802.09941v2)

Abstract: Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning.

• The paper’s main contribution is a comprehensive concurrency model based on the Work-Depth paradigm to analyze DNN training operations.
• It evaluates data, model, and pipeline parallelism strategies, demonstrating their effectiveness in accelerating deep neural network training.
• The study discusses communication optimizations and emerging trends such as neural architecture search for scalable, robust distributed learning.

Overview of "Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis"

The paper "Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis" provides a comprehensive review and analysis of parallel and distributed strategies for deep learning, focusing on techniques to accelerate deep neural network (DNN) training. It examines various methods from theoretical and practical perspectives, discussing asynchronous stochastic optimization, distributed systems, communication schemes, and neural architecture search. The survey outlines potential directions for parallelism in deep learning and considers the impact of these techniques on the field.

Key Findings

1. Concurrency Analysis:
   • The paper models different types of concurrency inherent in DNNs, from single operators to full network training and inference.
   • It employs the Work-Depth model to analyze average parallelism, highlighting significant work ($\mathbf{W}$) versus depth ($\mathbf{D}$) characteristics in operations.
2. Parallelization Strategies:
   • Data Parallelism: Divides the workload of tensor operations across multiple computational resources, effectively distributing training among samples in a minibatch.
   • Model Parallelism: Segments the DNN itself, distributing neurons or layers across processors for reduced memory overhead and enhanced computation.
   • Pipelining: Overlaps computations between layers or partitioned segments, efficiently using resources though at some cost to latency.
3. Distributed Training Architectures:
   • Discusses centralized and decentralized architectures, with parameter server (PS) infrastructures playing a crucial role in managing distributed training data.
   • Stale-synchronous and asynchronous methodologies effectively handle model consistency issues, preserving convergence through balanced, lag-tolerant updates.
4. Communication Optimization:
   • Strategies such as quantization and sparsification are explored for reducing communication bandwidth and improving efficiency in distributed systems.
   • Techniques like loss-less coding and precise numerical representation adjustments are effective in accelerating training with negligible accuracy loss.
5. Emerging Trends:
   • There is a significant focus on evolving strategies for automated architecture search via techniques like reinforcement learning and evolutionary algorithms.
   • The paper anticipates broader application of meta-optimization for hyper-parameter tuning, leveraging genetic algorithms and SMBO to explore deeper DNN configuration spaces.

Implications

The methodologies reviewed impact both theoretical and practical aspects of DNN deployment. As deep learning models grow in complexity and data scales, improving parallelism and distribution becomes critical. These advancements facilitate efficient use of high-performance computing infrastructure, thus enabling faster model training and improved generalization.

The exploration of communication-efficient strategies and optimization algorithms indicate a shift towards scalable, robust distributed solutions. The potential for automatically designing neural architectures presents paths toward more agile, adaptive deep learning systems.

Future Directions

The paper sets the stage for further research into:

• Hybrid strategies that seamlessly integrate data, model, and pipeline parallelism.
• More sophisticated tools for dynamic resource allocation in elastic computing frameworks.
• Advanced AI systems employing automated architecture search to discover highly optimized networks with minimal human intervention.

In conclusion, this comprehensive survey provides a valuable base for understanding current strategies and innovations in parallel and distributed deep learning, identifying avenues for future exploration to enhance AI capabilities.