A Feasibility Analysis of Power-Awareness and Energy Minimization in Modern Interconnects for High-Performance Computing
Bibliographic record
Abstract
High-performance computing (HPC) systems consume a significant amount of power, resulting in high operational costs, reduced reliability, and wasting of natural resources. Therefore, power consumption has become an increasingly important design constraint in high-performance clusters. In this regard, research on power-aware HPC has emerged. While most research has focused at understanding and utilizing applicationspsila behavior to scale down the CPU for energy savings, this paper demonstrates the positive impact of modern interconnects in delivering energy-efficiency in high-performance clusters. In this work, we first present the power-performance profiles of the Myrinet-2000 and Quadrics QsNetIIat the user-level and MPI-level in comparison to a traditional, non-offloaded Gigabit Ethernet. Such information enables us to devise a power-aware MPI runtime library that automatically and transparently performs message segmentation and re-assembly in order to increase energy savings. Secondly, by designing and evaluating a number of all-gather collectives, we argue that it is possible to increase the energy-efficiency of a cluster by optimizing its messaging layers.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".