Software pipelining loops with conditional branches
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
Software pipelining is an aggressive scheduling technique that generates efficient code for loops and is particularly effective for VLIW architectures. Few software pipelining algorithms, however, are able to efficiently schedule loops that contain conditional branches. We have developed an algorithm we call All Paths Pipelining (APP) that addresses this shortcoming of software pipelining. APP is designed to achieve optimal or near-optimal performance for any run of iterations while providing efficient code for transitioning between runs. A run is the execution of consecutive iterations that all execute the same path through a loop. APP accomplishes this by using techniques from modulo scheduling and kernel recognition algorithms, the two main approaches for software pipelining loops. We have implemented the APP algorithm in our research compiler and have evaluated its performance by executing its generated code on a VLIW instruction-set simulator. For a processor with five heterogeneous functional units, APP is able to add another 1% to 23% increase in performance over basic software pipelining by effectively pipelining loops with conditional branches.
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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 | 0.000 | 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