Intermediary Cache Eviction Policy for Raster Operations Data
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Solution Overview
Problem
In memory management, particularly in Level 2 cache systems, determining which data to evict is challenging due to varying data types and usage patterns from different clients, leading to inefficient cache eviction policies that result in cache misses and performance bottlenecks, especially in high-throughput applications like graphics processing.
Innovation Solution
A method for evicting data from an intermediary cache based on categorizing data as raster operations data or normal data, using eviction classes to optimize cache eviction decisions, thereby reducing command stalls and improving system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is evicted frequently to make room for new data, then cache space is available for new requests, but existing data is lost that might be needed soon, leading to more cache misses
Solution Approach 1:
The patent applies local quality by differentiating data into multiple eviction classes (e.g., frequently accessed vs. rarely accessed data) and applying different eviction strategies to each class. Instead of uniform eviction, the system prioritizes evicting less important data while retaining critical data, thus balancing cache space utilization with data availability needs.
Solution Approach 2:
The patent segments the cache into different eviction classes based on data characteristics and access patterns. By dividing data into segments with different eviction priorities, the system can independently manage each segment's eviction behavior, allowing aggressive eviction in some segments while being more conservative in others, thereby resolving the contradiction between space utilization and data availability.
2Reliability
If data is evicted infrequently to retain useful data, then data availability is maintained, but cache space fills up causing missed opportunities for new data
Solution Approach 1:
The patent applies local quality by differentiating data into multiple eviction classes (e.g., frequently accessed vs. rarely accessed data) and applying different eviction strategies to each class. Instead of uniform eviction, the system prioritizes evicting less important data while retaining critical data, thus balancing cache space utilization with data availability needs.
Solution Approach 2:
The patent segments the cache into different eviction classes based on data characteristics and access patterns. By dividing data into segments with different eviction priorities, the system can independently manage each segment's evacuation behavior, allowing aggressive eviction in some segments while being more conservative in others, thereby resolving the contradiction between space utilization and data availability.
3Ease of manufacture
If a simple eviction policy is used, then implementation is easy, but it cannot handle different data types and usage patterns from multiple clients
Solution Approach 1:
The patent segments the cache into different eviction classes based on data characteristics and access patterns. By dividing data into segments with different eviction priorities, the system can independently manage each segment's evacuation behavior, allowing aggressive eviction in some segments while being more conservative in others, thereby resolving the contradiction between space utilization and data availability.
Solution Approach 2:
The patent applies local quality by differentiating data into multiple eviction classes (e.g., frequently accessed vs. rarely accessed data) and applying different eviction strategies to each class. Instead of uniform eviction, the system prioritizes evicting less important data while retaining critical data, thus balancing cache space utilization with data availability needs.
4Productivity
If cache eviction is optimized for one data type, then performance for that data type improves, but performance deteriorates for other data types with different access patterns
Solution Approach 1:
The patent applies local quality by differentiating data into multiple eviction classes (e.g., frequently accessed vs. rarely accessed data) and applying different eviction strategies to each class. Instead of uniform eviction, the system prioritizes evicting less important data while retaining critical data, thus balancing cache space utilization with data availability needs.
Solution Approach 2:
The patent segments the cache into different evacuation classes based on data characteristics and access patterns. By dividing data into segments with different evacuation priorities, the system can independently manage each segment's evacuation behavior, allowing aggressive evacuation in some segments while being more conservative in others, thereby resolving the contradiction between space utilization and data availability.
Data Source
AI summary
One embodiment of the invention is a method for evicting data from an intermediary cache that includes the steps of receiving a command from a client, determining that there is a cache miss relative to the intermediary cache, identifying one or more cache lines within the intermediary cache to store data associated with the command, determining whether any of data residing in the one or more cache lines includes raster operations data or normal data, and causing the data residing in the one or more cache lines to be evicted or stalling the command based, at least in part, on whether the data includes raster operations data or normal data. Advantageously, the method allows a series of cache eviction policies based on how cached data is categorized and the eviction classes of the data. Consequently, more optimized eviction decisions may be made, leading to fewer command stalls and improved performance.