A memory transaction model for Sparse Matrix-Vector multiplications on GPUs
Bibliographic record
Abstract
The Sparse Matrix-Vector multiplication (SpMV) is an algorithm used in many fields. Since the introduction of CUDA and general purpose programming on GPUs, several efforts to optimize it have been reported. SpMV optimization is complex due to irregular memory accesses depending on the nonzero element distribution of the matrix. In this paper, we propose a model that predicts the number of memory transactions of SpMV for a matrix stored in the CSR format. With the number of memory transactions known in advance, the performance and the execution time can be estimated. The model can be used to select the best suited CUDA implementation for sparse matrices for a given application domain. Predicted results from the model are within 7.5% for the matrices of more than 1000 rows that we have tested on the NVIDIA Tesla K20c and Ge-Force GTX 670.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".