Artifacts for "CPU- and GPU-initiated Communication Strategies for Conjugate Gradient Methods on Large GPU Clusters"
Bibliographic record
Abstract
This dataset contains computational artifacts related to the paper: “CPU- and GPU-initiated Communication Strategies for Conjugate Gradient Methods on Large GPU Clusters” The paper describes computational experiments that were conducted to evaluate the performance of multi-GPU iterative linear solvers based on the conjugate gradient (CG) method. The computational artifacts are located in several subdirectories: 'aCG-1.0.0/' contains the source code for aCG (version 1.0.0), which implements of the various multi-GPU CG solvers that are used for the performance benchmarks presented in the paper. 'partitions/' contains input files related to partitioning and distributing matrices that were used in the experiments. Partitions were computed using METIS (Karypis and Kumar, 1998), a multilevel graph partitioner. From the SuiteSparse Collection (Davis and Hu, 2011), six matrices were selected: Bump_2911, Cube_Coup_dt6, Flan_1565, Queen_4147, Serena and audikw_1. For each matrix, partitions are provided for 2, 4, 8, 16 and 32 parts. 'scripts/' contains job scripts for submitting jobs on three clusters: LUMI, MareNostrum 5 and Wisteria/BDEC-01 (Aquarius). These scripts carry out performance measurements for the multi-GPU CG solvers in aCG and PETSc, and were used to collect the results presented in the paper. 'results/' contains results from the performance benchmarks presented in the paper as tables in a plain-text format. References Davis, T. A. and Y. Hu. 2011. “The University of Florida Sparse Matrix Collection”. ACM Transactions on Mathematical Software 38, 1, Article 1 (December 2011), 25 pages. DOI: https://doi.org/10.1145/2049662.2049663 Karypis, G., and V. Kumar. 1998. “A fast and high quality multilevel scheme for partitioning irregular graphs”. SIAM Journal on scientific Computing 20, 1, pp. 359–392. DOI: https://doi.org/10.1137/S1064827595287997
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".