Automatic task generation for the multi-level computing architecture
Why this work is in the frame
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Bibliographic record
Abstract
The Multi-Level Computing Architecture (MLCA) is a novel Parallel Programmable Systems-on-a-chip (PP-SoC) for multimedia applications, which promises to address the programmability challenge for PP-SoCs. The MLCA programming model requires that coarse-grain units of computation, or tasks, be identified and extracted out of sequential code. This paper describes an approach to automatically generating tasks from sequential programs to target the MLCA. The approach uses a new compiler pragma called Split to describe task boundaries in a sequential program. A compiler heuristic is developed to place this pragma in the program, effectively marking task boundaries. The compiler is then used to generate task code, ensuring correct control and data flow on the MLCA. Experimental evaluation of this approach, implemented in a prototype compiler and using realistic multimedia applications, shows that the approach is effective in extracting tasks out of sequential programs and that it results in MLCA programs whose performance is comparable to that of manually task--generated code.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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