Rotating Data-Sorting Network for Low-Latency Stream Processing
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Solution Overview
Problem
Traditional sorting methods become resource-intensive and inefficient when processing long sets or continuous streams of data, leading to increased memory usage and latency, and fail to maintain ordered lists effectively in real-time applications.
Innovation Solution
A rotating data-sorting network with a circular data structure and distributed nodes that operate in parallel, allowing for efficient sorting and maintenance of ordered lists by comparing new values to existing ones without the need for complete re-sorting, using a processor to coordinate and execute functions across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional sorting methods are used to process long sets or continuous streams of data, then sorting can be completed, but memory usage increases and latency increases
Solution Approach 1:
The sorting network is divided into multiple stages, each containing multiple parallel comparators. Data flows through these stages in a structured manner, with each stage performing a specific sorting function. This segmentation allows the system to process large data sets efficiently without requiring excessive memory resources at any single point.
Solution Approach 2:
The patent introduces a spatial dimension to the sorting process by arranging comparators in a multi-stage network topology. Instead of using a single sequential sorting algorithm that requires extensive memory, the data is distributed across multiple spatial locations (stages and parallel paths), transforming the memory-intensive problem into a spatially-distributed processing problem.
2Productivity
If traditional sorting methods are used to process long sets or continuous streams of data, then sorting can be completed, but latency increases
Solution Approach 1:
The sorting network is pre-configured with multiple stages and parallel comparator paths before data arrives. This preliminary structural preparation allows incoming data to be processed immediately through the predetermined sorting pathways, eliminating the need for dynamic algorithm selection or reconfiguration that would increase latency.
Solution Approach 2:
The multi-stage parallel comparator network enables continuous data flow through the sorting system. Multiple comparators operate simultaneously at different stages, maintaining continuous sorting action without idle periods. This continuous operation reduces latency by ensuring that data is constantly being processed rather than waiting for sequential operations to complete.
3Productivity
If distributed nodes operate in parallel for data sorting, then sorting efficiency improves, but device complexity increases
Solution Approach 1:
The complex sorting task is segmented into multiple independent stages, each with its own set of parallel comparators. This segmentation breaks down the overall system complexity into manageable modular units, where each stage can be designed and analyzed independently while contributing to the overall sorting functionality.
Solution Approach 2:
Each comparator unit in the network is designed as a universal building block that can be replicated across multiple stages and positions. This universality reduces design complexity by using the same basic component repeatedly rather than designing unique components for each position, while still achieving parallel processing efficiency.
Data Source
AI summary
A distributed resource parallel-operated rotating data-sorting network is disclosed. The rotating data-sorting network is an ultra-low latency design that may sift and sort values of interest from a continuous data stream or finite data set. The rotating data-sorting network includes a circular data structure that rotates through data sets and comprises a series of nodes.