Efficient Sequential Consistency in GPUs via Relativistic Cache Coherence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it