Task Entanglement Coding for Distributed Matrix Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed computing in machine learning faces challenges with stragglers, where slow edge devices hinder overall computation speed, and existing methods either ignore unfinished tasks or risk information leakage due to synchronization issues and lack of secure data transmission.
Innovation Solution
A distributed matrix multiplication method using task entanglement-based coding with Chebyshev polynomial codes, which encodes matrices for edge devices to enable partial results from stragglers to contribute to the final result and reduces data transmission by using fewer encoded matrices, ensuring secure computation through random matrix encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If synchronization systems assign one task to each edge device sequentially, then task management is simple, but overall computation speed is lowered due to stragglers
Solution Approach 1:
The patent divides the matrix computation task into multiple sub-tasks and assigns them to different edge devices simultaneously. Each edge device receives a portion of the computation workload, allowing parallel processing that eliminates the sequential bottleneck of synchronization systems while maintaining manageable task distribution through coded task assignment.
Solution Approach 2:
The main server performs preliminary encoding of tasks using coding theory before distribution to edge devices. This pre-processing creates coded task assignments that enable the system to tolerate stragglers and recover from failures, allowing faster overall computation by preparing robust task distributions in advance.
2Productivity
If copied computation tasks are assigned to several edge devices to solve stragglers, then computation speed improves, but unfinished tasks of stragglers are completely ignored
Solution Approach 1:
The patent changes the parameter of task completion threshold from requiring full task completion to accepting partial results. By using coding theory, the system can reconstruct the final computation result even when some edge devices finish earlier or partially complete tasks, thereby utilizing straggler results rather than ignoring them and improving both speed and reliability.
Solution Approach 2:
The system implements feedback mechanisms where the main server collects results from edge devices and uses coding theory to reconstruct the final output. This feedback loop allows the system to adapt to varying completion statuses of individual devices and still achieve the computation goal, turning straggler behavior from a problem into an acceptable variation in the distributed computation process.
3Reliability
If coded tasks are assigned in overlapping manner to handle stragglers, then computation robustness improves, but information leakage risk increases during communication
Solution Approach 1:
The patent introduces coded task assignments as an intermediary layer between the main server and edge devices. This intermediary uses coding theory to create tasks that can be executed independently on different devices while maintaining computational robustness. The coded structure acts as a mediator that enables reliable computation without requiring direct, vulnerable communication channels for task coordination.
Solution Approach 2:
By segmenting the computation into independent coded tasks distributed across edge devices, the system reduces the information exchange requirements between devices during execution. Each device works on its assigned coded task independently, minimizing communication vulnerabilities while the main server reconstructs the final result using the distributed coded results, thereby maintaining robustness without increasing information leakage risk.
4Reliability
If more encoded matrices are transmitted to edge devices to ensure computation accuracy, then computation reliability improves, but data transmission amount increases
Solution Approach 1:
The patent changes the parameter of encoding redundancy from traditional approaches to coding theory-based encoding. This parameter change allows the system to achieve computation reliability with fewer transmitted matrices by using coded task assignments that can reconstruct results from partial completions, thereby reducing data transmission quantity while maintaining accuracy through intelligent coding rather than brute-force redundancy.
Data Source
AI summary
A method of performing distributed matrix computation using task entanglement-based coding as a method of processing a huge amount of matrix computation in a distributed manner in a distributed computing environment is provided. A main server encodes information to be transmitted to a plurality of edge devices for distributed matrix computation on the basis of task entanglement-based coding employing a Chebyshev polynomial, thereby reducing the amount of information to be transmitted. Also, when the number of computation results received from the edge devices becomes a recovery threshold, the main server immediately performs decoding to derive a matrix computation result.