PTRAM: A Parallel Topology-and Routing-Aware Mapping Framework for Large-Scale HPC Systems
Bibliographic record
Abstract
With the rapid increase in the size and scale of modern systems, topology-aware process mapping has become an important approach for improving system efficiency. A poor placement of processes across compute nodes could cause significant congestion within the interconnect. In this paper, we propose a new greedy mapping heuristic as well as a mapping refinement algorithm. The heuristic attempts to minimize a hybrid metric that we use for evaluating various mappings, whereas the refinement algorithm attempts to reduce maximum congestion directly. Moreover, we take advantage of parallelism in the design and implementation of our proposed algorithms to achieve scalability. We also use the underlying routing information in addition to the topology of the system to derive a better evaluation of congestion. Our experimental results with 4096 processes show that the proposed approach can provide more than 60% improvement in various mapping metrics compared to an initial in-order mapping of processes. Communication time is also improved by 50%. In addition, we also compare our proposed algorithms with 4 other heuristics from the LibTopoMap library, and show that we can achieve better mappings at a significantly lower cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".