Inter-Core Locality Aware Memory Scheduling
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
Graphics Processing Units (GPUs) run thousands of parallel threads and achieve high Memory Level Parallelism (MLP). To support high Memory Level Parallelism, a structure called a Miss-Status Holding Register (MSHR) handles multiple in-flight miss requests. When multiple cores send requests to the same cache line, the requests are merged into one last level cache MSHR entry and only one memory request is sent to the Dynamic Random-Access Memory (DRAM). We call this inter-core locality. The main reason for inter-core locality is that multiple cores access shared read-only data within the same cache line. By prioritizing memory requests that have high inter-core locality, more threads resume execution. In this paper, we analyze the reason for inter-core locality and show that requests with inter-core locality are more critical to performance. We propose a GPU DRAM scheduler that exploits information about inter-core locality detected at the last level cache MSHRs. For high inter-core locality benchmarks this leads to an average 28 percent reduction in memory request latency and 11 percent improvement in performance.
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 | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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