Adaptive Map Data Compression for Faster Memory Read Caching
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Solution Overview
Problem
The existing memory systems face challenges in efficiently caching map data due to the trade-off between compression ratio and parsing time, which affects read performance, as high compression ratios increase parsing time but reduce hit rates, while low compression ratios improve parsing time but decrease hit rates.
Innovation Solution
The proposed memory system dynamically adjusts the compression ratio of map data based on the available memory capacity, using a compressor to compress data at higher ratios when memory is abundant and lower ratios when memory is scarce, thereby optimizing parsing time and hit rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high compression ratio is used for map data, then memory capacity utilization is improved, but parsing time increases
Solution Approach 1:
The system dynamically adjusts the compression ratio based on available memory capacity. When memory capacity is sufficient, a lower compression ratio is applied to reduce parsing time. When memory capacity is limited, a higher compression ratio is applied to maximize memory utilization. This dynamic adjustment resolves the contradiction by making the compression ratio adaptive rather than fixed.
Solution Approach 2:
The compression ratio parameter is changed based on memory capacity conditions. The system monitors available memory capacity and selects appropriate compression ratios from a set of predefined ratios. This parameter change allows the system to optimize between memory utilization and parsing time depending on current memory conditions.
2Loss of time
If low compression ratio is used for map data, then parsing time is reduced, but memory capacity utilization decreases
Solution Approach 1:
The system dynamically adjusts the compression ratio based on available memory capacity. When memory capacity is sufficient, a lower compression ratio is applied to reduce parsing time. When memory capacity is limited, a higher compression ratio is applied to maximize memory utilization. This dynamic adjustment resolves the contradiction by making the compression ratio adaptive rather than fixed.
Solution Approach 2:
The compression ratio parameter is changed based on memory capacity conditions. The system monitors available memory capacity and selects appropriate compression ratios from a set of predefined ratios. This parameter change allows the system to optimize between memory utilization and parsing time depending on current memory conditions.
3Productivity
If map data is cached in memory, then read performance is improved, but memory capacity is consumed
Solution Approach 1:
The system changes the compression ratio parameter based on available memory capacity to optimize the balance between cached data quantity and memory consumption. By adjusting this parameter, the system can cache more map data when memory is abundant while maintaining acceptable parsing performance, and cache less data with higher compression when memory is scarce.
Solution Approach 2:
The caching strategy is made dynamic by adjusting compression ratios according to real-time memory capacity conditions. This allows the system to adaptively manage the trade-off between read performance improvement and memory capacity consumption.
Data Source
AI summary
An operating method of a memory system may include: searching for, in a memory, target map data corresponding to the read request; loading the target map data from a memory device when the target map data are not searched; compressing the loaded target map data using a predetermined compression ratio depending on an available capacity of the memory; caching the compressed target map data in the memory; parsing the compressed target map data; reading target user data corresponding to the read request from the memory device based on the parsed target map data; and outputting the read target user data.