Hot spare components for performance-cost improvement in multi-core SIMT
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
Adding redundant components is a well known technique for replacing defective components either before shipment or in the field, resulting yield improvement and consequently cost reduction. However, most yield improvement strategies utilize redundant components only when another component fails (i.e., cold spares). In this paper, we investigate the cost and performance implications of employing hot spares in multi-core single-instruction, multiple-thread (SIMT) processors. Hot spares are available to increase yield (and reduce costs) when the components are defective; otherwise, they can be used to improve performance in the field. Starting with a baseline architecture with six cores, and 32 lanes each, we added three hot spare cores, with two lanes each. When we make the lanes of the hot spares available to replace defective lanes in the baseline cores, we observe that expected performance per cost improved more than 2.5 and 1.7 times relative to systems integrating no redundancy and cold spares, respectively.
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.
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