Dynamic Block Reconfiguration for Distributed Machine Learning Convergence
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Solution Overview
Problem
Existing distributed machine learning schemes fail to efficiently converge parameters due to uneven convergence rates among workers, leading to increased calculation time as some parameters require more optimization than others.
Innovation Solution
A distributed machine learning device and method that dynamically reconfigures parameter blocks based on convergence degrees, redistributing parameters from heavily loaded blocks to lightly loaded ones to equalize convergence rates across workers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If parameters are divided into blocks and optimized by multiple workers in parallel, then processing speed is improved, but convergence time increases due to uneven distribution of convergence rates among workers
Solution Approach 1:
The patent implements dynamic block configuration where the assignment of parameters to workers is not fixed but changes over time based on convergence status. The management device monitors convergence rates and redistributes parameters dynamically, allowing the system to adapt to changing optimization needs during the learning process.
Solution Approach 2:
The system incorporates feedback mechanisms where the management device receives information about convergence rates from workers and uses this feedback to adjust block configurations. This closed-loop control ensures that parameters requiring more optimization are continuously reassigned to appropriate workers, preventing idle time and balancing the workload.
2Ease of operation
If parameters are evenly distributed among workers, then initial processing is balanced, but total convergence time increases due to ignoring individual convergence rates
Solution Approach 1:
The patent applies local quality by treating different parameter blocks with different optimization strategies based on their specific convergence characteristics. Each worker receives parameter blocks tailored to their current capacity and the convergence needs of those parameters, rather than a uniform distribution.
Solution Approach 2:
The system changes the assignment parameters (which parameters are assigned to which worker) based on observed convergence rates. The management device adjusts the block configuration by monitoring how quickly parameters are converging and reassigning them to optimize overall system performance.
3Device complexity
If fixed block configuration is used for parallel optimization, then implementation is simple, but optimization efficiency decreases due to unequal convergence rates
Solution Approach 1:
The system implements a self-service mechanism where the management device automatically monitors convergence rates and performs reassignment without external intervention. The workers continue their optimization tasks while the management device handles the dynamic configuration adjustments based on real-time performance data.
Data Source
AI summary
Provided is a distributed machine learning device which can efficiently converge a plurality of parameters. The distributed machine learning device includes: block configuration determination unit that determines a plurality of configurations of blocks each being a subset of a plurality of parameters used for a prediction model, and acquires a change amount of each subset of parameters belonging to the block, based on a result of executing processing of optimizing the plurality of parameters, and changes an element of the subset of the parameters in the block, based on the acquired change amount.