L2P Table Capacity Scaling With Hash-Based PPN Mapping
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Solution Overview
Problem
Data storage management systems face challenges in processing commands efficiently as the logical-to-physical (L2P) look-up table (LUT) scales non-linearly with increasing drive sizes, leading to increased computational demand and power consumption.
Innovation Solution
The L2P LUT is split into MSBs handled by a conventional L2P LUT and LSBs handled by a hash function, with conflict resolution mechanisms in meta areas to maintain linear scaling and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the L2P LUT size is increased to accommodate larger drive sizes, then the mapping capacity is improved, but the computational demand and power consumption increase non-linearly
Solution Approach 1:
The L2P LUT is segmented into two parts: MSBs (most significant bits) stored in the L2P LUT and LSBs (least significant bits) generated by a hash function. This segmentation allows the L2P LUT to scale linearly with drive size while the hash function handles the remaining address bits, preventing non-linear growth in computational demand and power consumption.
Solution Approach 2:
The patent changes the parameter representation by separating the physical address into MSBs and LSBs. The MSBs are stored in the L2P LUT while LSBs are generated through hashing. This parameter transformation enables the system to maintain linear scaling characteristics even as drive capacity increases.
2Quantity of substance
If the L2P LUT size is increased to accommodate larger drive sizes, then the mapping capacity is improved, but the computational complexity increases
Solution Approach 1:
The address mapping process is segmented into two independent operations: L2P LUT lookup using MSBs and hash function computation for LSBs. This segmentation reduces computational complexity by avoiding the need to process the entire address through a single large LUT, thereby simplifying the overall computational workflow.
Solution Approach 2:
The hash function acts as an intermediary that generates LSBs based on the LPN and MSBs. This intermediary component simplifies the mapping process by decoupling the address generation from the L2P LUT lookup, reducing the computational burden on the main system.
3Productivity
If hash function is used to generate LSBs, then linear scaling is achieved, but conflict resolution complexity increases
Solution Approach 1:
Conflict resolution information is prepared in advance and stored in the meta area during manufacturing or initialization. This preliminary action allows conflicts to be resolved quickly during operation without adding real-time computational complexity, maintaining linear scaling efficiency.
Solution Approach 2:
The meta area contains pre-computed conflict resolution data that enables the system to self-resolve hash conflicts without external intervention or complex real-time computation. This self-service mechanism maintains scaling efficiency while handling conflicts.
Data Source
AI summary
Methods and devices are provided in which a controller of a storage device receives a command with a corresponding logical page number (LPN). The LPN is mapped to a physical page number (PPN) in a storage medium of the storage device based on a logical-to-physical (L2P) look-up table (LUT) including most significant bits (MSBs) of a PPN entry. Least significant bits (LSBs) of the PPN entry are generated from the LPN based on a hash function. The PPN for the command is determined based on the MSBs, the LSBs, and meta area in the storage medium.