Bibliographic record
Abstract
In response to the current requirements of energy efficiency and high performance in computing systems, architects have turned towards customization. General purpose computing however remains a challenge as processors must adhere to a variety of applications, on-chip resources, and increased performance without solely relying on transistor scaling and additional cache levels. For this reason, the concept of Reconfigurable Computing Unit (RCU) processors have been proposed which redesign the conventional processor on the microarchitectural and architectural level. RCUs are extended in this work to support a multi-task workload using OmpSs, where task and instruction placement algorithms are thoroughly assessed for effects of performance and energy efficiency. Experimental results demonstrate that a single RCU processor with a double engine configuration is able to exceed single-core performance on average by 1.48x and achieve/exceed dual-core performance. The various inter-and intra-task placement algorithms tested also display up to 16.7% and 23% fluctuation in performance and energy efficiency, respectively, depending on the method and RCU engine combination employed.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".