Namespace L2P Mapping Granularity for Mixed-Write Memory Subsystems
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Solution Overview
Problem
Existing memory sub-systems inefficiently manage namespace mapping, leading to suboptimal allocation of memory resources for namespace L2P tables, affecting performance and cost efficiency, particularly in scenarios with both sequential and random writes.
Innovation Solution
Adaptive memory allocation for namespace L2P tables based on parameters such as namespace capacity and granularity, dynamically adjusting memory usage according to the write patterns of each namespace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If memory is allocated for namespace L2P tables based on maximum capacity, then all namespaces can be supported, but memory resources are wasted when namespaces are not fully utilized
Solution Approach 1:
The patent implements dynamic memory allocation for namespace L2P tables by introducing a granularity parameter that allows the memory allocation to adapt to the actual usage patterns of each namespace. Instead of static allocation based on maximum capacity, the system dynamically adjusts memory allocation according to the granularity setting, which determines the mapping resolution between namespace logical addresses and device physical addresses. This enables the memory allocation to scale with actual namespace utilization rather than predetermined maximums.
Solution Approach 2:
The patent changes the allocation parameters from fixed maximum capacity to variable granularity-based allocation. By introducing granularity as a configurable parameter (e.g., 128 KB, 256 KB, 512 KB), the system can adjust the memory allocation granularity to match the actual namespace requirements. This parameter change allows fine-tuned control over memory usage, allocating more memory only when higher granularity is needed for specific namespaces while using less memory for namespaces that can operate with coarser granularity.
2Productivity
If finer granularity is used for namespace L2P tables, then random write performance improves, but memory consumption increases
Solution Approach 1:
The patent applies local quality by allowing different granularity settings for different namespaces based on their specific access patterns and performance requirements. Instead of using a uniform fine granularity across all namespaces, the system configures granularity locally for each namespace according to its workload characteristics. Namespaces with random write workloads can use finer granularity to improve performance, while namespaces with sequential access patterns can use coarser granularity to reduce memory consumption.
3Adaptability or versatility
If more memory is allocated for namespace metadata, then namespace management flexibility increases, but cost efficiency decreases
Solution Approach 1:
The patent implements partial action by allocating memory resources partially based on actual namespace requirements rather than providing full memory allocation for all possible namespaces. The granularity-based allocation ensures that memory is allocated only to the extent needed for each namespace's operational requirements, avoiding excessive memory provisioning. This partial allocation approach maintains sufficient namespace management flexibility while reducing overall memory consumption and associated costs.
Data Source
AI summary
A system includes a first memory device; a second memory device; and a processing device, operatively coupled with the first memory device and the second memory device, to perform operations including: determining a size of the first memory device designated for storing a plurality of address mapping data structures, wherein each address mapping data structure of the plurality of address mapping data structures is associated with a corresponding namespace of a plurality of namespaces, wherein each namespace of the plurality of namespaces is associated with a corresponding region of a second memory device; associating a first address mapping granularity level with the first memory device for storing a first address mapping data structure of the plurality of address mapping data structures; determining a remaining available space of the first memory device by excluding a first size of the first address mapping data structure from the size of the first memory device, wherein the first size is calculated based on the first address mapping granularity level; and associating, in view of the remaining available space of the first memory device and a remaining available space of the second memory device, a second address mapping granularity level with the first memory device for storing a second address mapping data structure of the plurality of address mapping data structures.


