Efficient Sequential Consistency in GPUs via Relativistic Cache Coherence
Bibliographic record
Abstract
Recent work has argued that sequential consistency (SC) in GPUs can perform on par with weak memory models, provided ordering stalls are made less frequent by relaxing ordering for private and read-only data. In this paper, we address the complementary problem of reducing stall latencies for both read-only and read-write data. We find that SC stalls are particularly problematic for workloads with inter-workgroup sharing, and occur primarily due to earlier stores in the same thread; a substantial part of the overhead comes from the need to stall until write permissions are obtained (to ensure write atomicity). To address this, we propose RCC, a GPU coherence protocol which grants write permissions without stalling but can still be used to implement SC. RCC uses logical timestamps to determine a global memory order and L1 read permissions; even though each core may see a different logical "time," SC ordering can still be maintained. Unlike previous GPU SC proposals, our design does not require invasive core changes and additional per-core storage to classify read-only/private data. For workloads with interworkgroup sharing overall performance is 29% better and energy is 25% less than in best previous GPU SC proposals, and within 7% of the best non-SC design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".