Combining Run-Time Checks and Compile-Time Analysis to Improve Control Flow Auto-Vectorization
Bibliographic record
Abstract
SIMD (Single Instruction Multiple Data) instructions apply the same operation to multiple elements simultaneously. Compilers transform codes to exploit SIMD instructions through auto-vectorization. Control flow can lead to challenges for auto-vectorization tools because compilers conservatively assume branches are divergent. However, it is common that all SIMD lanes follow the same control-path at run-time, a property we call dynamic uniformity. In this paper, we present VecRC (an auto-vectorizer with run-time checks), a novel compile-time technique that uses run-time checks to test for dynamically uniform control flows. Under the assumption of dynamic uniformity, we perform several compile-time analyses that improve control flow auto-vectorization vs state-of-the-art approaches. VecRC leverages dynamic uniformity to vectorize loops with control-dependent loop-carried dependences. Existing strategies use speculation to optimistically execute vector code, and must correct any incorrect computation due to violated run-time assumptions. VecRC performs compile-time analysis based on uniformity to support such dependences without the overhead of speculation. We propose a probability-based cost model to predict the profitability of run-time checks to eliminate the need for specialized profiling or expensive auto-tuning required in existing methods. VecRC is evaluated in LLVM on a diverse range of benchmarks including SPEC2017, NPB, Parboil, TSVC, and Rodinia on Intel Skylake and IBM Power 9 architectures. On the Skylake architecture, geometric mean speedups of 1.31x, 1.20x, 1.19x, and 1.06x over Region Vectorizer, GCC, Clang, and ICC are obtained with VecRC on real benchmark code.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".