ahSpMV: An Autotuning Hybrid Computing Scheme for SpMV on the Sunway Architecture
Bibliographic record
Abstract
The prevalence of the Internet of Things (IoT) and the explosion of available information on the Web have led to an enormous amount of widely available IoT data sets with sparsity. Sparse matrix-vector multiplication (SpMV) is one of the most essential algorithms in various kinds of IoT applications. This article designs an autotuning hybrid computing scheme for SpMV, named ahSpMV, on the powerful and unique architecture of Sunway TaihuLight supercomputer, to combine the advantages of the heterogeneous parallel Sunway architecture and the Hybrid (HYB) sparse matrix format and optimize the SpMV's performance. First, we propose a heterogeneous parallelization design for ahSpMV based on the heterogeneous manycore architecture of the SW26010 of Sunway TaihuLight and the hybrid feature of the HYB format. Second, we propose several optimization techniques for computation and communication of ahSpMV, to fully utilize the computing power of Sunway. Third, we analyze the execution time of ahSpMV on Sunway. Fourth, based on the performance analysis, we propose an autotuning scheme for ahSpMV to set the proper parameter for the HYB format. We evaluate ahSpMV's performance on the Sunway architecture. The result analysis indicates that ahSpMV has obvious performance improvement over parallel SpMV based on other related sparse matrix formats. The optimization techniques and the autotuning scheme for ahSpMV also yield expected optimization effects. Moreover, the experimental results illustrate that ahSpMV has good scalability on the Sunway architecture.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".