A Feasibility Analysis of Power-Awareness and Energy Minimization in Modern Interconnects for High-Performance Computing
Why this work is in the frame
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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 QsNet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</sup> at 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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it