Symbiotic Task Scheduling and Data Prefetching
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
Task-parallel programming models enable programmers to extract parallelism from irregular applications.Since software-based taskparallel runtimes impose crippling overheads on fine-grain tasks, architects have designed manycores with hardware support for task management.These hardware task-parallel systems can scale challenging workloads to hundreds of cores, but fail to use conventional prefetchers due to short (100-cycle) tasks.Lacking prefetching, they often expose DRAM latency to applications, fumbling the performance gains of hardware.We present the Task-Seeded Prefetcher (TSP) and Memory Response Task Scheduler (MRS), a symbiotic pair that boost performance in general-purpose task-parallel hardware.TSP learns and prefetches the data-access pattern of each task function, seeded with its descriptor that is queued by the task scheduler.MRS augments the baseline task-to-core dispatch policy by using prefetch status from TSP to optimize core utilization.Together, TSP and MRS provide speedups of up to 3.1 (gmeans up to 1.4) across 13 benchmarks on 256-core task-parallel systems that were already 3-60 faster than parallel software.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.006 |
| 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