Parallel Data Processing with Sparsity-Aware Transfer Encoding
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Solution Overview
Problem
Existing machine learning systems face significant data transfer times that slow down overall processing due to the transfer of large volumes of data, particularly in massively parallel systems, where a substantial fraction of the data is identical or matches predetermined criteria, leading to increased computing resource usage and latency from data compression.
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 computing resources and reducing processing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data compression is applied to reduce data transfer volume, then data transfer time is reduced, but computing resource usage increases and processing latency increases
Solution Approach 1:
The patent extracts only the essential information about data sparsity patterns and transmits indicators rather than the full data itself. By taking out only the necessary metadata about which data elements are zero or identical, the system reduces transfer volume without requiring compression algorithms that consume computing resources.
Solution Approach 2:
Instead of copying the full data values, the system creates and transmits a compressed representation or indicator that points to the actual data locations. This copying approach transmits only the sparsity pattern information rather than the full data payload, reducing transfer requirements.
2Reliability
If all data is transmitted in parallel systems, then data completeness is maintained, but data transfer time increases significantly
Solution Approach 1:
The patent segments data transmission by separating dense data (non-zero values) from sparse data (zero or identical values). By segmenting the data into these categories and transmitting only the dense portions while using indicators for sparse portions, the system maintains data completeness through structured representation while reducing transfer time.
Solution Approach 2:
The system introduces an intermediary indicator structure that acts as a mediator between the full data and the transmission protocol. This intermediary layer conveys sparsity information and enables the receiver to reconstruct the complete data set without transmitting all data values explicitly.
3Quantity of substance
If data compression algorithms are used, then data transfer volume is reduced, but processing latency increases due to compression and decompression operations
Solution Approach 1:
The system employs a self-service approach where the data structure itself encodes the sparsity information without requiring external compression algorithms. The indicator format is inherently designed to represent both dense and sparse data, eliminating the need for separate compression/decompression processing steps.
Solution Approach 2:
The patent changes the representation parameter from raw data values to indicator-based encoding that directly represents sparsity patterns. This parameter transformation allows the data to be transmitted in a compact form without requiring algorithmic compression, thereby avoiding processing latency.
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.