HiSpMV: Hybrid Row Distribution and Vector Buffering for Imbalanced SpMV Acceleration on FPGAs
Bibliographic record
Abstract
Sparse matrix-vector multiplication (SpMV) is a fundamental operation in numerous applications such as scientific computing, machine learning, and graph analytics. While recent studies have made great progress in accelerating SpMV on HBM-equipped FPGAs, there are still multiple remaining challenges to efficiently accelerate imbalanced SpMV where the distribution of non-zeros in the sparse matrix is imbalanced across different rows. First, the imbalanced workload distribution among the parallel processing elements (PEs) leads to PE under-utilization and performance degradation. Second, the read-after-write dependency of the long-latency floating-point accumulation on the output vector causes pipeline stalls inside the PE, and existing scheduling solutions for balanced matrices no longer work effectively for imbalanced ones. Third, the memory access latency for the often overlooked input vector becomes a new performance bottleneck after the SpMV acceleration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".