Science per Dollar: Modeling Emerging Node Architectures for Accelerator-centric Computing
Bibliographic record
Abstract
New High Performance Computing (HPC) node architectures such as superchips and Composable Disaggregated Infrastructure have recently emerged as potential directions for the future of supercomputing. However, with new architectures available at different costs, it can be difficult to forecast the correct design decision to maximize science performed per dollar. In this paper, we present a model designed to determine the performance of workloads on different node architectures with a fixed budget. We demonstrate how this model can be applied to narrow design decisions when using a variety of workloads including molecular dynamics applications, machine learning applications, a heat diffusion simulator, and an atomic reactor simulator. Using detailed traces of GPU operations, we justify the components of the model and determine if the model is accurate enough to have decision assisting capability. Specifically, we analyze the results of the model in relation to the GPU utilization and data movement of the workloads and discuss other factors to consider when choosing a node architecture for a datacenter. We find that the model can aid in deciding which node architectures to consider for a supercomputer deployment, using detailed tracing of a variety of representative applications and proxy applications for HPC to demonstrate the utility of the model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".