CASpMV: A Customized and Accelerative SpMV Framework for the Sunway TaihuLight
Why this work is in the frame
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Bibliographic record
Abstract
The Sunway TaihuLight, equipped with 10 million cores, is currently the world's third fastest supercomputer. SpMV is one of core algorithms in many high-performance computing applications. This paper implements a fine-grained design for generic parallel SpMV based on the special Sunway architecture and finds three main performance limitations, i.e., storage limitation, load imbalance, and huge overhead of irregular memory accesses. To address these problems, this paper introduces a customized and accelerative framework for SpMV (CASpMV) on the Sunway. The CASpMV customizes an auto-tuning four-way partition scheme for SpMV based on the proposed statistical model, which describes the sparse matrix structure characteristics, to make it better fit in with the computing architecture and memory hierarchy of the Sunway. Moreover, the CASpMV provides an accelerative method and customized optimizations to avoid irregular memory accesses and further improve its performance on the Sunway. Our CASpMV achieves a performance improvement that ranges from 588.05 to 2118.62 percent over the generic parallel SpMV on a CG (which corresponds to an MPI process) of the Sunway on average and has good scalability on multiple CGs. The performance comparisons of the CASpMV with state-of-the-art methods on the Sunway indicate that the sparsity and irregularity of data structures have less impact on CASpMV.
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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 | 0.001 | 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 it