A Fully Structure-Driven Performance Analysis of Sparse Matrix-Vector Multiplication
Bibliographic record
Abstract
Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific, machine-learning, and other compute-intensive applications. Performance characteristics, however, depend on a complex combination of storage format, machine capabilities, and choices in code-generation. A deep understanding of the relative impact of these properties is important in itself, and also to better understanding the performance potential of alternative execution contexts such as web-based scientific computing, where the recent introduction ofWebAssembly offers the potential for low-level, near-native performance within a web browser. In this work we characterize the performance of SpMV operations for different sparse storage formats based on the sparse matrix structure and the machine architecture. We extract structural properties from 2000 real-life sparse matrices to understand their impact on the choice of storage format and also on the performance within those storage formats for both WebAssembly and native C. We extend this with new matrix features based on a "reuse-distance" concept to identify performance bottlenecks, and evaluate the effect of interaction between the matrix structure and hardware characteristics on SpMV performance. Our study provides valuable insights to scientific programmers and library developers to apply best practices and guide future optimization for SpMV in general, and in particular for web-based contexts with abstracted hardware and storage models.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".