Parameter Synchronization Mode Switching for Deep Learning Efficiency
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Solution Overview
Problem
Deep learning processes in distributed computing environments face inefficiencies in parameter synchronization due to the difficulty in simultaneous analysis of data between GPUs and CPUs, affecting cross-platform services.
Innovation Solution
A parameter synchronization method that determines the time ratio of data transfer and CPU phases in deep learning iterative processes, switching between 'copy mode' and 'parameter server mode' based on this ratio to optimize workload distribution between CPUs and GPUs, and providing configuration notifications for system improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data exchange between GPU and CPU is increased to improve deep learning training, then parameter synchronization efficiency deteriorates due to difficulty in simultaneous analysis
Solution Approach 1:
The patent segments the iterative process into distinct phases (H2D, D2H, CPU, FW, BW) and further divides each phase into sub-phases with specific start and end events. This segmentation allows independent analysis and optimization of each phase's data exchange operations, resolving the contradiction by making the complex synchronization process manageable through structured breakdown.
Solution Approach 2:
The patent dynamically adjusts the synchronization mode between 'copy mode' and 'parameter server mode' based on the calculated time ratio of data transfer phases. This dynamic adaptation allows the system to optimize parameter synchronization efficiency in real-time according to actual workload characteristics, improving both training efficiency and synchronization performance.
2Adaptability or versatility
If frequent parameter exchange between computers in clusters is performed, then cross-platform service capability improves, but parameter synchronization time increases
Solution Approach 1:
The patent changes the operational parameters of parameter synchronization by switching between two distinct modes: 'copy mode' for frequent small-scale exchanges and 'parameter server mode' for less frequent larger-scale synchronizations. This parameter adjustment optimizes the balance between cross-platform service capability and synchronization time based on actual needs.
Solution Approach 2:
The patent performs preliminary analysis of iterative process phases and pre-determines the optimal synchronization mode before actual parameter exchange occurs. By calculating time ratios and identifying phase characteristics in advance, the system prepares the most efficient synchronization approach, reducing actual synchronization time while maintaining cross-platform capability.
Data Source
AI summary
A parameter synchronization method is implemented in a computing device. The parameter synchronization method includes importing a deep learning training task of a preset model into a server communicatively coupled to the computing device, recording a preset number of iterative processes during the deep learning training, dividing each iterative process into a number of phases according to time, determining whether a time ratio of an H2D phase, a D2H phase, and a CPU phase in each iterative process is greater than a preset value, and confirming the server to use a copy mode for performing parameter synchronization when the time ratio of the H2D, D2H, and CPU phases is determined to be greater than the preset value.