Loop Detection in Cuckoo Hash Tables via Graph Depth Analysis
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Solution Overview
Problem
Cuckoo hashing systems face performance issues due to the potential creation of infinite loops when inserting entries, leading to increased memory read and write costs, which can affect communication system performance.
Innovation Solution
A method and system to detect the presence of a loop before inserting an entry into a Cuckoo hash table by generating a graph representing hash locations and calculating depths, allowing for the determination of whether an entry would create a loop, and storing potentially conflicting entries in a stash to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If entries are inserted into a Cuckoo hash table using standard cuckoo hashing, then the hash table can store and retrieve data efficiently, but infinite loops may be created during insertion leading to increased memory read and write costs
Solution Approach 1:
The patent applies preliminary action by performing loop detection before completing the insertion operation. The system calculates the depth value for the new entry and compares it with the minimum depth of existing entries to predict whether a loop will be created. If a loop is predicted, the insertion is prevented in advance, avoiding the infinite loop problem while maintaining insertion efficiency.
Solution Approach 2:
The patent introduces depth calculation as an intermediary mechanism to detect potential loops. By calculating and comparing depth values of entries, the system can identify configurations that would create loops without actually executing the insertion. This intermediary depth comparison acts as a predictor to prevent harmful loop creation while allowing efficient insertions.
2Reliability
If loop detection mechanisms are added to prevent infinite loops, then reliability is improved, but device complexity increases due to additional graph generation and depth calculation operations
Solution Approach 1:
The patent changes the parameter approach by using depth values as a predictive metric for loop detection. Instead of complex graph traversal or simulation, the system calculates a simple depth parameter for each entry and compares it with existing minimum depths. This parameter-based approach significantly reduces the complexity of the loop detection mechanism while maintaining high reliability in loop prevention.
3Reliability
If depth calculation and loop prediction are performed for each insertion, then loop creation is prevented, but memory access costs increase due to additional calculations and comparisons
Solution Approach 1:
The patent applies partial action by performing only the necessary depth calculation and comparison operations needed for loop detection, rather than executing full graph traversal or simulation. The system calculates depth values and compares them with existing minimum depths, performing just enough work to predict loop creation without excessive computation. This partial approach reduces memory access costs while maintaining effective loop prevention.
Data Source
AI summary
A system includes a first storage, a second storage, and a processor. The first storage is configured to store a Cuckoo hash table which includes a plurality of locations. The second storage is configured to store a graph including a plurality of nodes. The processor coupled to the first storage and the second storage is configured to map each of the locations in the Cuckoo hash table to each of the nodes in the graph, and to determine whether a first entry to be added to a first location in the Cuckoo hash table creates a loop in the graph by executing a filter module. More particularly, the processor is to execute the filter module by detecting a presence of the loop before the first entry to occupy the first location in the Cuckoo hash table, the first location associated with a node, in the graph, occupied by a second entry.