DYNAMIC LOAD BALANCERS FOR A MULTITHREADED MULTIPROCESSOR SYSTEM
Bibliographic record
Abstract
Designing multi-processor systems that deliver a reasonable price-performance ratio using off-the-shelf processor and compiler technologies is a major challenge. For an important class of applications, it is critical to explore fine-grain parallelism to achieve reasonable performance. In such parallel systems it is essential to efficiently manage communication latencies, bandwidth, and synchronization overheads. In this paper we study load balancing strategies for the runtime system of a multi-threaded system. EARTH (Efficient Architecture for Running Threads) is a multi-threaded programming and execution model that supports fine-grain, non-preemptive, threads in a distributed memory environment. We describe the design and implementation of a set of dynamic load balancing algorithms, and study their performance in divide-and-conquer, regular, and irregular applications. Our experimental study on the distributed memory multi-processor IBP SP-2 indicate that a randomized load balancer perform as well as, and often better than, history based load balancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".