On the Dynamic Scheduling of Task Farm Patterns on a Heterogeneous CPU-GPGPU Environment
Bibliographic record
Abstract
Heterogeneous clusters and multi-core environments are gradually surpassing the homogeneous systems due to their high performance and flexibility. Task scheduling in these systems is an extensively studied subject. However, in a heterogeneous architecture consisting of multi-core CPUs and many-core GPGPUs (General Purpose Graphics Processor Units), task mapping becomes much more complex due to differences in architectures and programming models among the processors. Consequently, designing a scheduler which facilities a balanced distribution of loads by taking full advantage of the processing power of a CPU-GPGPU system is nontrivial. In this paper we discuss a multi-round scheduling algorithm and a scheduling framework for farm-pattern based applications on such a system. This is an important step towards designing a full-scale pattern-based scheduler to automatically and efficiently map the parallel tasks to the heterogeneous processors. As a proof of concept, we have designed a scheduling framework for the task-farm pattern based applications. The framework provides the necessary "separation of concerns" and hides the underlying complex scheduling details from the programmer. The experimental results demonstrate that our dynamic scheduling algorithm achieves better to similar performances as compared to some of the well-known scheduling algorithms for CPU-GPGPU systems.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".