Directory Capacity and Sharer Scaling: Theory Deep Dive

Theory deep dive

Theory Deep Dive for Directory Capacity and Sharer Scaling explains how to reason from coherency invariant to measurable engineering decision.

The core theory is that coherency is a distributed consistency contract. Every optimization must preserve legal visibility and progress under adversarial interleavings.

Canonical flow

COHERENCY DECISION FLOW — Directory Capacity and Sharer Scaling

request intent (read/shared/unique/writeback/evict)
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ownership check + sharer metadata evaluation
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snoop / directory action + ordering gate
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data source selection (owner forward vs memory)
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state transition + acknowledgment closure
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metric validation + regression guardrails

Cache coherency deep dive

Cache coherence is a correctness contract across caches, interconnect, and software ordering.

Concept diagram

requester -> coherence fabric -> owner or memory -> state update

Metric graph

traffic mix across request, snoop, response, data

Metrics and artifacts to collect

• coherence latency

• invalidation rate

• retry rate

• stale-read incidents

Mini case study

Anchor debug to first stale read and the exact line state transition.

Debug branches

• Track ownership

• Track ordering

• Track evidence

Senior review question

Ask: what is the first line state transition that deviates, and which ordering rule does it break?

Key takeaways

• Tie every coherency claim to one cache line, one transaction identity, and one measurable counter.

• Keep proof artifacts from simulation and silicon replay aligned by address, state, and ordering event.

Common pitfalls

• Chasing bandwidth regressions without checking false sharing and line bouncing first.

• Assuming coherence correctness implies memory consistency correctness.

• Declaring closure without litmus, stress, and post-silicon replay evidence.