Minimizing memory requirements in rate-optimal schedules
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Bibliographic record
Abstract
We address the problem of minimizing buffer storage requirement in constructing rate-optimal compile-time schedules for multi-rate dataflow graphs. We demonstrate that this problem, called the Minimum Buffer Rate-Optimal (MBRO) scheduling problem, con be formulated as a unified linear programming problem. A novel feature of the method is that it tries to minimize the memory requirement while simultaneously maximizing the computation rate. We have constructed an experimental testbed which implements our scheduling algorithm as well as: the widely used periodic admissible parallel schedules proposed by R.A. Lee and D.A. Messerschmitt (1987); the optimal scheduling buffer allocation (OSBA) algorithm of Q. Ning and G.R. Gao (1993); and the multi-rate software pipelining (MRSP) algorithm (R. Govinderajan and G.R. Gao, 1993). The experimental results have demonstrated a significant improvement in buffer requirements for the MBRO schedules compared to the schedules generated by the other three methods. Compared to block schedules, MBRO schedules perform better in terms of both computation rate and buffer requirements. The average observed improvement in buffer storage is 26% with respect to the MRSP algorithm, 17% with respect to block schedules and 5% with respect to the OSBA formulation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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