Affinity scheduling of unbalanced workloads
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
Scheduling in a shared memory multiprocessor is often complicated by the fact that a unit of work may be processed more efficiently on one processor than on any other, due to factors such as the presence of required data in a local cache. The unit of work is said to have an "affinity" for the given processor, in such a case. The scheduling issue that has to be considered is the tradeoff between the goals of respecting processor affinities (so as to obtain improved efficiencies in execution) and of dynamically assigning each unit of work to whichever processor happens to be, at the time, least loaded (so as to obtain better load balance and decreased processor idle times). A specific context in which the above scheduling issue arises is that of shared memory multiprocessors with large per-processor caches or cached main memories. The shared-memory programming paradigm of such machines permits the dynamic scheduling of work. The data required by a unit of work may, however, often reside mostly in the cache of one particular processor, to which that unit of work thus has affinity. In this paper, two new "affinity scheduling" algorithms are proposed for a context in which the units of work have widely varying execution times. The two proposed algorithms are: (1) dynamic partitioned affinity scheduling and (2) wrapped partitioned affinity scheduling. An experimental study of these algorithms finds them to perform well in this context.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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