Finding the Best HPCMP Architectures Using Benchmark Application Results for TI-09
Bibliographic record
Abstract
Time-to-solution and scalability of an application can vary greatly from one computer architecture to another. It is important then to consider the appropriateness of architectures with respect to applications to make the most efficient use of available resources. However, the number of applications currently used on systems within the High Performance Computing Modernization Program (HPCMP) is very large. This paper, therefore, focuses solely on codes within the HPCMP Technology Insertion 2009 (TI-09) Applications Benchmarking Suite. These codes include Adaptive Mesh Refinement (AMR), Air Vehicles Unstructured Solver (AVUS), CTH, General Atomic and Molecular Electronic Structure System (GAMESS), HYbrid Coordinate Ocean Model (HYCOM), Improved Concurrent Electromagnetic Particle In Cell (ICEPIC), and Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This paper evaluates the parallel performance-per-processor of each of these seven codes on major systems within the Program. This, in turn, can guide users and developers in selecting appropriate architectures for each code.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".