Parallel Data Transmission Using Sparse Pattern Indicators
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Solution Overview
Problem
Existing machine learning systems face significant data transfer times due to the transfer of large volumes of data, particularly in massively parallel systems, which can slow down overall processing capabilities and introduce latency with traditional data compression methods.
Innovation Solution
Implementing sparsity-aware communication protocols that transmit indicators of sparse data rather than the data itself, reducing the volume of data transferred and eliminating the need for data compression, thereby optimizing data transmission and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data compression methods are used to reduce data transfer volume, then data transfer efficiency is improved, but processing latency increases due to compression and decompression overhead
Solution Approach 1:
The patent extracts and transmits only the essential sparsity pattern information (indices of non-zero elements) rather than the complete compressed data. This allows the receiver to reconstruct the original data structure without performing full decompression operations, thereby reducing processing latency while still achieving data transfer efficiency.
Solution Approach 2:
Instead of compressing data and then transmitting it (traditional approach), the patent inverts the approach by first identifying sparsity patterns and transmitting only those patterns. This reversal eliminates the need for decompression and directly provides the receiver with the essential structural information needed for efficient data reconstruction.
2Quantity of substance
If large volumes of data are transferred in massively parallel systems, then complete data availability is improved, but data transfer time increases significantly
Solution Approach 1:
The patent extracts only the critical sparsity pattern information from the large data volumes and transmits this extracted information. By separating the essential structural metadata from the bulk data, the system achieves complete data availability at the receiver while dramatically reducing the actual transmission time.
Solution Approach 2:
The patent changes the parameter being transmitted from the full data values to sparsity pattern indicators (such as indices or masks). This parameter transformation reduces the transmission payload from potentially millions of data points to a much smaller set of positional or structural indicators, thereby reducing transfer time while preserving data completeness.
Data Source
AI summary
Systems and methods for transmitting and processing data can use representations of data portions (e.g., blocks, chunks, or other subunits of data) that match a specified pattern, such as zero gradients in a machine learning training algorithm. These representations can allow different parts of a system to communicate the existence of these data portions to each other without actually transmitting the data portions while also allowing for the transmission of data portions that do not match the specified pattern. Processing of data can also use these representations or indicators as placeholders for the omitted data and perform calculations based on tallies, skipped memory locations, or other ways of accounting for the omitted data. This can in some cases reduce computing resources used to process data, such as data that may have been communicated using such representations.