Tensor Comparison via Locality Sensitive Hashing
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Solution Overview
Problem
In distributed machine learning environments, the iterative transmission of large tensor objects between remotely distributed processes consumes significant communication resources, and existing compression techniques are not always necessary, leading to wastage of computation and communication resources, especially when tensors are similar.
Innovation Solution
The method determines the similarity of tensor objects by encoding each vector as a (length, angle) pair using locality sensitive hashing, allowing processes to decide whether to send tensors based on similarity, thereby reducing communication overhead and enhancing data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression and decompression techniques are used to transmit tensors between distributed processes, then the size of transmitted data is reduced, but computation resources are wasted on unnecessary compression and decompression cycles when tensors are similar
Solution Approach 1:
The system performs preliminary comparison of tensor parameters (lengths and angles) before initiating compression and transmission. By checking whether tensors are similar in advance, the system avoids unnecessary compression and decompression operations, thereby reducing computation resource waste while still achieving efficient data transmission when needed
2Reliability
If all tensors are transmitted between distributed processes, then data accuracy is maintained, but communication resources are consumed excessively
Solution Approach 1:
The system extracts only the essential parameters (lengths and angles) from tensors for comparison purposes. By working with these extracted parameters rather than complete tensors, the system can determine similarity with minimal communication overhead and only transmit full tensors when they are actually different, thus maintaining accuracy while conserving communication resources
3Measurement precision
If tensor similarity comparison is performed using complete tensor data, then accuracy of similarity determination is improved, but communication and computation resources are wasted
Solution Approach 1:
The system creates simplified copies of tensors in the form of parameter pairs (lengths and angles) that can be compared efficiently. These parameter copies capture the essential characteristics needed for similarity determination without requiring transmission or processing of complete tensor data, thereby achieving accurate comparison with minimal resource consumption
Data Source
AI summary
Tensor comparison across a network by determining a first parameter of a first vector representation of a first tensor object associated with a first processor, receiving a second parameter associated with a second vector representation of a second tensor object associated with a second processor, determining a first difference between the first parameter and the second parameter, and sending the first vector representation from the first processor to the second processor, according to the first difference.