Compressed Cache Dictionary Sharing for Low-Latency Capacity Gains
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cache systems face challenges in maximizing cache capacity while minimizing access latency and power consumption, as they often require larger caches that incur higher latency and energy costs, and traditional compression techniques struggle to effectively exploit data value locality for efficient compression.
Innovation Solution
The implementation of a flexible dictionary sharing mechanism in compressed caches, where each cache line is mapped to a set with a dictionary that achieves the best compression ratio, using a dictionary selector unit to evaluate multiple candidate dictionaries and store the selected dictionary's ID in the tag array, allowing for dynamic remapping of cache lines to optimize compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If larger caches are used to increase cache capacity, then more data can be stored, but access latency increases and power consumption increases
Solution Approach 1:
The cache is divided into multiple sets, each with its own dictionary for compression. This segmentation allows parallel access to different cache sets, maintaining low latency while increasing overall capacity through compression in each segment.
Solution Approach 2:
The patent changes the compression parameters by allowing different compression ratios and dictionary sizes for different cache sets based on workload characteristics. This enables optimal balance between capacity, latency, and power consumption for different data patterns.
2Quantity of substance
If larger caches are used to increase cache capacity, then more data can be stored, but power consumption increases
Solution Approach 1:
The cache is divided into multiple sets, each with its own dictionary for compression. This segmentation allows parallel access to different cache sets, maintaining low latency while increasing overall capacity through compression in each segment.
Solution Approach 2:
The patent changes the compression parameters by allowing different compression ratios and dictionary sizes for different cache sets based on workload characteristics. This enables optimal balance between capacity, latency, and power consumption for different data patterns.
3Loss of substance
If traditional compression techniques are used, then some space savings are achieved, but data value locality is not effectively exploited for efficient compression
Solution Approach 1:
The patent implements feedback mechanisms where the compression performance is continuously monitored and used to dynamically adjust dictionary selection and remap cache lines to optimize compression ratios. This feedback loop ensures that data value locality is effectively exploited for efficient compression.
Solution Approach 2:
The patent introduces dynamic dictionary selection and cache line remapping based on workload characteristics and compression performance. This dynamic approach allows the system to adapt to changing data patterns and effectively exploit data value locality for efficient compression.
Data Source
AI summary
Systems, apparatuses, and methods for implementing flexible dictionary sharing techniques for caches are disclosed. A set-associative cache includes a dictionary for each data array set. When a cache line is to be allocated in the cache, a cache controller determines to which set a base index of the cache line address maps. Then, a selector unit determines which dictionary of a group of dictionaries stored by those sets neighboring this set would achieve the most compression for the cache line. This dictionary is then selected to compress the cache line. An offset is added to the base index of the cache line to generate a full index in order to map the cache line to the set corresponding to this chosen dictionary. The compressed cache line is stored in this set with the chosen dictionary, and the offset is stored in the corresponding tag array entry.