Cache Dirty-State Transfer Using Replacement Policy Signals
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Solution Overview
Problem
Managing dirty data in cache coherence protocols is challenging due to the need to determine which agent should retain or pass the dirty state, leading to inefficient writebacks and increased power consumption.
Innovation Solution
A processor system that uses cache replacement policy information to dynamically decide whether to retain or pass the dirty state based on the likelihood of the data being held in the cache, leveraging existing replacement policies to optimize writeback timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the dirty state is passed to the requestor, then the snoopee can free cache space, but the requestor may not need the data long enough to justify the writeback overhead
Solution Approach 1:
The patent applies dynamics by making the dirty state transfer decision adaptive rather than static. The snoopee evaluates replacement policy information in real-time to determine whether to pass or retain the dirty state, allowing the system to dynamically adjust to varying cache conditions and workload characteristics.
Solution Approach 2:
The patent uses preliminary action by evaluating replacement policy information before making the dirty state transfer decision. This pre-assessment allows the system to predict whether the requestor will retain the cache line long enough to justify the writeback, preventing unnecessary energy consumption from premature writebacks.
2Loss of energy
If the dirty state is retained by the snoopee, then unnecessary writebacks are avoided, but the snoopee cache may fill up requiring eviction of potentially useful data
Solution Approach 1:
The system dynamically adjusts the dirty state retention decision based on current cache conditions and replacement policy information. This allows the snoopee to optimize between retaining dirty data to avoid writebacks and freeing cache space to accommodate new data, adapting to varying system states.
Solution Approach 2:
The patent incorporates feedback by using replacement policy information to inform the dirty state transfer decision. This feedback mechanism allows the system to learn from past cache behavior and make more informed decisions about when to pass or retain dirty states, improving overall cache efficiency.
3Productivity
If replacement policy information is used to decide dirty state transfer, then writeback timing is optimized, but additional complexity is introduced to the cache coherence protocol
Solution Approach 1:
The patent applies universality by using the existing replacement policy information for multiple purposes: both for cache line replacement decisions and for determining dirty state transfer. This multi-functionality approach avoids adding dedicated complexity for dirty state management while still achieving optimized writeback timing.
Solution Approach 2:
The system uses self-service by leveraging the snoopee's existing replacement policy logic to make the dirty state transfer decision. Rather than introducing a separate decision-making mechanism, the existing replacement policy infrastructure serves the additional function of guiding dirty state management.
Data Source
AI summary
Embodiments of the present disclosure include techniques for managing dirty data. An agent receives a request for data. If the data is dirty data, the agent may use a replacement policy to determine if the data should be passed clean or dirty to the requestor. The replacement policy may correspond to how long the dirty data being stored in a cache line is to be maintained. In one embodiment, the replacement policy is a circuit, such as an SRAM and a logic circuit, for example.