Towards a Programming Paradigm for Reconfigurable Computing: Asynchronous Graph Programming
Bibliographic record
Abstract
The shift towards reconfigurable systems-hardware and software that adapt themselves to an external context-promises to unlock unprecedented performance, power consumption, and quality of service. However, reconfiguration imposes several challenges on the design of cyber-physical systems. Current design practices, including software frameworks and programming languages, are ill-prepared for supporting reconfiguration. In this paper, we explore Asynchronous Graph Programming, a programming paradigm and an associated model of computation designed for efficient and automated parallelization across processing elements, efficient reconfiguration (i.e., mapping of computational tasks across processing elements), and combining synchronous and asynchronous I/O handling within the same conceptual programming model. We also introduce an analytical model of parallelization, unlocked by Asynchronous Graph Programming, that can inform reconfiguration strategies. We analyze the implications of our model through an analysis of reconfiguration scenarios given a program's characteristics; our analysis quantifies the benefits of reconfiguring software for higher levels of parallelism, given an amount of data left to process. We also introduce Horde, an open source Asynchronous Graph Programming interpreter, and use it to empirically validate the performance advantage of its automatic parallelism capabilities; in our experiments, automatic parallelization from one to four cores improves average case execution time by a factor of 2 and worst case execution time by a factor of 3.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".