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
SIMT accelerators are equipped with thousands of computational resources. Conventional accelerators, however, fail to fully utilize available resources due to branch and memory divergences. This underutilization is manifested in two underlying inefficiencies: pipeline width underutilization and pipeline depth underutilization. Width underutilization occurs when SIMD execution units are not entirely utilized due to branch divergences. This affects lane activity and results in SIMD inefficiency. Depth underutilization takes place when the pipeline runs out of active threads and is forced to leave pipeline stages idle. This work addresses both inefficiencies by harnessing inactive threads available to the pipeline. We introduce Harnessing inActive thReads in many-core Processors (or simply HARP) to improve width and depth utilization in accelerators. We show how using inactive yet ready threads can enhance performance. Moreover, we investigate implementation details and study microarchitectural changes needed to build a HARP-enhanced accelerator. Furthermore, we evaluate HARP under a variety of microarchitectural design points. We measure the area overhead associated with HARP and compare to conventional alternatives. Under Fermi-like GPUs, we show that HARP provides 10% speedup on average (maximum of 1.6X) at the cost of 3.5% area overhead. Our analysis shows that HARP performs better under narrower SIMD and shorter pipelines.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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