Inter-Iteration Scheduling for Overlapped Distributed DNN Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training large deep neural network (DNN) models on multiple chips faces challenges such as scalability, hardware resource utilization efficiency, and computational overhead, particularly in distributed training scenarios, where existing solutions fail to optimize execution schedules for high hardware utilization.
Innovation Solution
The proposed method involves categorizing operations into computation-bound, memory-bound, I/O-bound, and network-bound types, and employing inter-iteration scheduling to overlap operations across heterogeneous compute environments, using a staleness-aware distributed optimizer to generate an execution schedule that prioritizes resource utilization and reduces communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed training is used to train large DNN models on multiple chips, then model scalability is improved, but hardware resource utilization efficiency deteriorates
Solution Approach 1:
The system performs preliminary classification of operations into computation-bound, memory-bound, I/O-bound, and network-bound categories before execution. This preliminary action enables the scheduler to make informed decisions about operation placement and timing, optimizing hardware utilization while maintaining scalability across distributed chips
Solution Approach 2:
The scheduler dynamically adjusts the execution schedule based on operation dependencies and hardware capabilities. By continuously monitoring operation status and adapting the execution plan in real-time, the system optimizes hardware resource allocation across distributed training nodes, resolving the contradiction between scalability and utilization efficiency
2Ease of operation
If operations are executed sequentially in distributed training, then execution simplicity is maintained, but training time increases
Solution Approach 1:
The training operations are segmented into distinct categories (computation-bound, memory-bound, I/O-bound, network-bound) based on their resource requirements. This segmentation enables independent optimization and parallel execution of different operation types, reducing training time while maintaining manageable execution complexity through structured organization
Solution Approach 2:
The system performs preliminary classification and scheduling of operations before execution begins. By analyzing operation dependencies and resource requirements in advance, the system creates an optimized execution plan that minimizes training time while keeping the execution process simple and manageable through automated scheduling
3Reliability
If communication operations are prioritized in distributed training, then coordination accuracy is improved, but computational throughput deteriorates
Solution Approach 1:
Operations are segmented into four distinct categories based on their primary resource constraints. This segmentation allows the scheduler to apply different prioritization strategies to each category, ensuring communication accuracy for network-bound operations while maintaining computational throughput for computation-bound operations through independent optimization
Solution Approach 2:
The scheduling system dynamically adjusts priorities based on the current execution state and operation dependencies. By monitoring real-time conditions, the system can prioritize communication operations when coordination is critical while allowing computational operations to proceed when throughput is the bottleneck, resolving the contradiction through adaptive prioritization
Data Source
AI summary
Disclosed is a technical solution to accelerate deep learning with inter-iteration scheduling based on operation categorization associated with the deep learning. An example apparatus includes interface circuitry, programmable circuitry; and instructions to cause the programmable circuitry to: classify a group of operations of a distributed deep learning workload based on a resource utilization of the group of operations; select at least two operations of the group of operations for overlapped execution based on the classification and a dependency analysis of the at least two operations of the group of operations; and perform a distributed training of the distributed deep learning workload based on an execution schedule that includes overlapped execution of the selected at least two operations.