Managing DRAM Latency Divergence in Irregular GPGPU Applications
Why this work is in the frame
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Bibliographic record
Abstract
Memory controllers in modern GPUs aggressively reorder requests for high bandwidth usage, often interleaving requests from different warps. This leads to high variance in the latency of different requests issued by the threads of a warp. Since a warp in a SIMT architecture can proceed only when all of its memory requests are returned by memory, such latency divergence causes significant slowdown when running irregular GPGPU applications. To solve this issue, we propose memory scheduling mechanisms that avoid inter-warp interference in the DRAM system to reduce the average memory stall latency experienced by warps. We further reduce latency divergence through mechanisms that coordinate scheduling decisions across multiple independent memory channels. Finally we show that carefully orchestrating the memory scheduling policy can achieve low average latency for warps, without compromising bandwidth utilization. Our combined scheme yields a 10.1% performance improvement for irregular GPGPU workloads relative to a throughput-optimized GPU memory controller.
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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