RISC-V Vectorization Coverage for HPC: A TSVC-Based Analysis
Bibliographic record
Abstract
The RISC-V Vector Extension (RVV) introduces scalable, vector-length agnostic operations with strong potential for high-performance computing (HPC). This paper presents a TSVC-based instruction coverage analysis of RVV to evaluate current compiler auto-vectorization support. We compile TSVC with GNU and LLVM under both vector-length agnostic (VLA) and vector-length specific (VLS) modes and analyze the emitted instructions against the RVV 1.0 specification. Our results quantify instruction usage across key groups, identify missed instructions, and classify the causes of failed vectorization, including compiler backend limitations, absent use cases in TSVC, and nontrivial or unsupported patterns. We also highlight TSVC’s limitations, including ambiguous kernel vectorizability and missing representations of modern HPC-relevant patterns. Finally, we suggest directions for enhancing benchmark suites to better reflect RVV capabilities and guide compiler development for HPC workloads.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".