High-Performance GPU Transactional Memory via Eager Conflict Detection
Bibliographic record
Abstract
GPUs transactional memory (TM) proposals to date have relied on lazy, value-based conflict detection, assuming that GPUs can amortize the latency by executing other warps. In practice, however, concurrency must be throttled to a few warps per core to avoid high abort rates, and TM performance has remained far below that of fine-grained locks. We trace this to the latency cost of validating transactions: two round trips across the crossbar required for most commits and aborts. With limited concurrency, the warp scheduler cannot amortize this, and leaves the core idle most of the time. In this paper, we show that value-based validation does not scale to high thread counts, and eager conflict detection becomes more efficient as the number of threads grows. We leverage this insight to propose GETM, a GPU TM with eager conflict detection. GETM relies on a novel distributed logical clock scheme to implement eager conflict detection without the need for cache coherence or signature broadcasts. GETM is up to 2.1 times faster than the state-of-the art prior work WarpTM (gmean 1.2 times), with 3.6 times lower silicon area overheads and 2.2 times lower power overheads.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".