Dynamic Batch Allocation in Deep Learning Nodes
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Solution Overview
Problem
In deep learning systems with multiple nodes, processing speed variations due to hardware performance and temperature differences lead to waiting times and reduced processing performance, as nodes may complete tasks at different rates, affecting overall learning efficiency.
Innovation Solution
The solution involves dynamically adjusting the number of batches and learning rate based on each node's performance, allowing for real-time optimization of batch processing and learning rate adjustments to synchronize node operations, thereby eliminating waiting times and enhancing overall learning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If parallel calculations are performed using multiple nodes to handle high computational complexity, then processing capability is improved, but processing speed variations between nodes cause waiting times and reduce overall efficiency
Solution Approach 1:
The patent dynamically adjusts the number of batches allocated to each node based on its processing speed. Fast nodes receive more batches while slow nodes receive fewer batches, creating a dynamic allocation strategy that adapts to real-time performance variations. This resolves the contradiction by making the system flexible rather than static, allowing full utilization of computational power without idle waiting time.
Solution Approach 2:
The patent changes the parameter of batch allocation from a fixed equal distribution to a variable distribution based on node performance metrics. By monitoring processing speeds and adjusting batch counts accordingly, the system transforms the rigid parameter into a flexible one that optimizes overall learning efficiency while maintaining high computational power utilization.
2Speed
If the number of batches is increased to improve learning throughput, then processing speed is improved, but nodes with different performance levels experience waiting times that reduce overall efficiency
Solution Approach 1:
The patent applies local quality by assigning different batch counts to different nodes based on their individual performance characteristics. Instead of uniform batch allocation, each node receives a customized batch count matched to its processing capability. This local optimization eliminates waiting times caused by performance mismatches while maintaining high overall processing speed.
Solution Approach 2:
The patent performs preliminary measurement of node processing speeds before batch allocation. By measuring and ranking node performance in advance, the system can pre-determine optimal batch assignments that prevent waiting times. This preliminary action allows the system to avoid the waiting time problem before it occurs during the learning process.
3Device complexity
If learning is performed with fixed batch allocation across nodes, then system simplicity is maintained, but performance variations between nodes reduce learning efficiency
Solution Approach 1:
The patent implements a feedback mechanism where node processing speeds are measured and used to adjust batch allocations. The system continuously monitors performance metrics and uses this feedback to optimize batch distribution. This feedback loop resolves the contradiction by adding only the minimal necessary complexity to achieve significant improvements in learning efficiency.
Data Source
AI summary
A non-transitory computer-readable recording medium storing a program for causing a computer to execute a procedure, the procedure includes in learning by a plurality of nodes in deep learning, determining to allocate a number of batches according to a performance of each of the plurality of nodes to the each of the plurality of nodes or to terminate the learning at a predetermined timing, and adjusting a learning rate to be used for the learning according to a ratio of a preset number of batches for the plurality of nodes to a number of execution batches executed by the allocation in the plurality of nodes or number of execution batches executed before the predetermined timing.