Reproducability Artifact for Running SLATE's GEMM and POTRF Operations on Summit and Crusher
Bibliographic record
Abstract
This artifact's aim is to reproduce results reported in our paper. The results are obtained on two systems located at OLCF. This artifact contains a folder for each system as follows: summit/ install-slate-on-summit.sh run-slate-on-summit.sh slate/ crusher/ install-slate-on-crusher.sh run-slate-on-crusher.sh slate/ Each folder has an installation script to install SLATE. The installation scripts can be run as follows: cd summit/ cd slate/ source ../install-slate-on-summit.sh The installation script for Crusher install-slate-on-crusher.sh can be run in the same way. Note that these installation scripts must be sourced since they load system modules and change environment variables. These modules and environment variables are also required by the experiments. The following commands are used to reproduce the gemm and potrf results on Summit: cd summit/slate/ bsub ../run-slate-on-summit.sh The following commands are used to reproduce the gemm and potrf results on Crusher: cd crusher/slate/ bash ../run-slate-on-crusher.sh Note that the account numbers in run-slate-on-....sh scripts must be set accordingly. The largest number in the gflop/s column in the result output is reported as the performance of the kernel operation on a corresponding number of nodes of the system.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.288 | 0.237 |
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".