aeSpTV: An Adaptive and Efficient Framework for Sparse Tensor-Vector Product Kernel on a High-Performance Computing Platform
Bibliographic record
Abstract
Multi-dimensional, large-scale, and sparse data, which can be neatly represented by sparse tensors, are increasingly used in various applications such as data analysis and machine learning. A high-performance sparse tensor-vector product (SpTV), one of the most fundamental operations of processing sparse tensors, is necessary for improving efficiency of related applications. In this article, we propose aeSpTV, an adaptive and efficient SpTV framework on Sunway TaihuLight supercomputer, to solve several challenges of optimizing SpTVon high-performance computing platforms. First, to map SpTV to Sunway architecture and tame expensive memory access latency and parallel writing conflict due to the intrinsic irregularity of SpTV, we introduce an adaptive SpTV parallelization. Second, to co-execute with the parallelization design while still ensuring high efficiency, we design a sparse tensor data structure named CSSoCR. Third, based on the adaptive SpTV parallelization with the novel tensor data structure, we present an autotuner that chooses the most befitting tensor partitioning method for aeSpTV using the variance analysis theory of mathematical statistics to achieve load balance. Fourth, to further leverage the computing power of Sunway, we propose customized optimizations for aeSpTV. Experimental results show that aeSpTV yields good sacalability on both thread-level and process-level parallelism of Sunway. It achieves a maximum GFLOPS of 195.69 on 128 processes. Additionally, it is proved that optimization effects of the partitioning autotuner and optimization techniques are remarkable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".