Multiplexer Tree Indexing for Parallel Data Access
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Solution Overview
Problem
Conventional hashed indexing in processing systems is a serial process that leads to a pinch point followed by a huge fan-out, resulting in inefficiencies in accessing data due to the sequential completion of hash levels and increased time required for read access as the volume of data grows.
Innovation Solution
The multiplexer tree indexing method performs index hashing and row reduction in parallel by using each address bit as a select bit only once in a particular path, avoiding the pinch point and fan-out issues, and ensuring fair hash distribution across all table entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional hashed indexing is used, then data can be stored and accessed, but the serial processing of hash levels creates a pinch point followed by huge fan-out, resulting in increased access time and reduced efficiency
Solution Approach 1:
The indexing structure is segmented into multiple independent multiplexer trees organized in a hierarchical fashion. Each tree handles a portion of the address space, allowing parallel processing of different segments simultaneously. This segmentation eliminates the single pinch point by distributing the hashing and selection operations across multiple independent paths.
Solution Approach 2:
The patent transitions from a single-dimensional serial hash chain to a multi-dimensional hierarchical structure. By organizing multiplexer trees in levels and allowing parallel traversal of different tree paths, the system adds a spatial dimension to the indexing process, enabling simultaneous evaluation of multiple index candidates rather than sequential processing.
2Quantity of substance
If the volume of data increases, then more storage capacity is available, but the length of indices increases and searching and matching takes longer
Solution Approach 1:
Large data structures are divided into multiple smaller multiplexer trees, each managing a subset of the total data. This segmentation allows the indexing operation to be distributed across multiple parallel processing paths, reducing the time required to search through large volumes of data while maintaining full storage capacity.
Solution Approach 2:
The system pre-organizes data into hierarchical multiplexer tree structures with predetermined paths and selection logic. This preliminary organization enables faster searching by eliminating the need for linear scanning or sequential comparison, as the parallel tree structure预先 establishes efficient access paths for retrieving data regardless of volume.
3Device complexity
If traditional hashed indexing is used, then indexing can be performed, but the serial completion of each hash level creates a bottleneck that reduces access speed
Solution Approach 1:
The single bottlenecked hash indexing structure is segmented into multiple independent multiplexer trees. Each tree operates independently with its own hashing and selection logic, allowing parallel execution of indexing operations. This segmentation maintains manageable device complexity for each individual tree while achieving high-speed access through parallelism.
Solution Approach 2:
Multiple multiplexer trees are merged into a hierarchical structure where the outputs of parallel tree operations are combined to form the final index. This merging of parallel processing paths enables the system to maintain the simplicity of individual hash operations while achieving the speed benefits of parallel execution across multiple trees.
Data Source
AI summary
Described herein is a system and method for multiplexer tree (muxtree) indexing. Muxtree indexing performs hashing and row reduction in parallel by use of at least one bit in a lookup address at least once in a particular path of the muxtree. The muxtree indexing generates a different final index as compared to conventional hashed indexing but still results in a fair hash, where all table entries get used with equal distribution with uniformly random selects.