SPATL: Honey, I Shrunk the Coherence Directory
Bibliographic record
Abstract
One of the key scalability challenges of on-chip coherence in a multicore chip is the coherence directory, which provides information on sharing of cache blocks. Shadow tags that duplicate entire private cache tag arrays are widely used to minimize area overhead, but require an energy-intensive associative search to obtain the sharing information. Recent research proposed a Tagless directory, which uses bloom filters to summarize the tags in a cache set. The Tagless directory associates the sharing vector with the bloom filter buckets to completely eliminate the associative lookup and reduce the directory overhead. However, Tagless still uses a full map sharing vector to represent the sharing information, resulting in remaining area and energy challenges with increasing core counts. In this paper, we first show that due to the regular nature of applications, many bloom filters essentially replicate the same sharing pattern. We next exploit the pattern commonality and propose SPATL (Sharing-pattern based Tagless Directory). SPATL exploits the sharing pattern commonality to decouple the sharing patterns from the bloom filters and eliminates the redundant copies of sharing patterns. SPATL works with both inclusive and noninclusive shared caches and provides 34% storage savings over Tagless, the previous most storage-efficient directory, at 16 cores. We study multiple strategies to periodically eliminate the false sharing that comes from combining sharing pattern compression with Tagless, and demonstrate that SPATL can achieve the same level of false sharers as Tagless with 5% extra bandwidth. Finally, we demonstrate that SPATL scales even better than an idealized directory and can support 1024-core chips with less than 1% of the private cache space for data parallel applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".