Tiered Memory Balanced Binary Search Tree Node Placement
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Solution Overview
Problem
Existing data structures and algorithms for dynamic searching in computer systems are not optimized for tiered memory architectures, leading to suboptimal performance due to inefficient use of faster and slower memory tiers.
Innovation Solution
Implementing a balanced binary search tree that dynamically rearranges nodes between faster and slower memory tiers, ensuring that the most frequently accessed nodes are stored in the faster memory tier, thereby reducing the number of memory accesses in the slower tier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in slower but cheaper memory tiers, then storage capacity increases and cost decreases, but access time increases
Solution Approach 1:
The patent segments the binary search tree into multiple tiers corresponding to different memory levels. Each tier stores a portion of the tree nodes, with frequently accessed nodes (those closer to the root) stored in faster memory tiers and less frequently accessed nodes stored in slower memory tiers. This segmentation allows the system to utilize both fast and slow memory resources while optimizing access patterns.
Solution Approach 2:
The patent applies local quality by assigning different storage locations to different parts of the data structure based on their access characteristics. Nodes that are more likely to be accessed (such as root nodes and nodes with high query frequency) are placed in faster memory tiers, while nodes with lower access probability are placed in slower memory tiers. This creates a non-uniform distribution of data across memory tiers that optimizes overall access performance.
2Speed
If frequently accessed data is placed in faster memory tiers, then access time decreases, but memory cost increases
Solution Approach 1:
The patent implements a dynamic data structure where nodes can be moved between memory tiers based on their access patterns. When a node is accessed, it can be promoted to a faster memory tier, and when access frequency decreases, it can be demoted to a slower memory tier. This dynamic adjustment allows the system to adapt to changing access patterns and optimize the trade-off between access speed and memory cost over time.
Solution Approach 2:
The patent changes the physical location parameter of data nodes between different memory tiers based on access frequency and other criteria. By dynamically adjusting where nodes are stored in the memory hierarchy, the system can optimize the balance between using expensive fast memory and cheaper slow memory, thereby reducing overall memory cost while maintaining acceptable access times.
3Ease of manufacture
If standard binary search tree algorithms are used without tiered memory optimization, then implementation simplicity is maintained, but search performance deteriorates
Solution Approach 1:
The patent segments the binary search tree operations into tier-aware operations that consider the memory hierarchy. Instead of treating all nodes uniformly, the algorithms are modified to account for which tier each node resides in, enabling optimizations such as preferring to traverse nodes in faster tiers and using techniques like node promotion and demotion to improve access patterns.
Solution Approach 2:
The patent introduces tier management as an intermediary layer between the logical binary search tree operations and the physical memory storage. This intermediary handles the complexity of managing nodes across multiple memory tiers, including decisions about where to store nodes, how to move nodes between tiers, and how to optimize traversal patterns. This abstraction allows the rest of the system to use standard BST algorithms while still benefiting from tiered memory optimization.
Data Source
AI summary
In one set of embodiments, a computer system can receive a request to insert or delete a key into or from a plurality of keys maintained by a dynamic search data structure, where the dynamic search data structure is implemented using a balanced binary search tree (BBST) comprising a plurality of nodes corresponding to the plurality of keys, where a first subset of the plurality of nodes are stored in the first memory tier, and where a second subset of the plurality of nodes are stored in the second memory tier. The computer system can further execute the request to insert or delete the key, where the executing results in a change in height of at least one node in the plurality of nodes. In response to the executing, the computer system can move one or more nodes in the plurality of nodes between the first and second memory tiers, the moving causing a threshold number of nodes of highest height in the BBST to be stored in the first memory tier.