Compiler optimizations for processors with SIMD instructions
Bibliographic record
Abstract
Abstract To achieve maximum efficiency, modern embedded processors for media applications exploit single instruction multiple data (SIMD) instructions. SIMD instructions provide a form of vectorization where a large machine word is viewed as a vector of subwords and the same operation is performed on all subwords in parallel. Systematic usage of SIMD instructions can significantly improve program performance. With C becoming the dominant language for programming embedded devices, there is a clear need for C compilers that use SIMD instructions whenever appropriate. However, SIMD instructions typically require each memory access to be aligned with the instruction's data access size. Therefore an important problem in designing the compiler is to determine whether a C pointer is aligned, i.e. whether it refers to the beginning of a machine word. In this paper, we describe our SIMD generation algorithm and present an analysis method which determines the alignment of pointers at compile time. The alignment information is used to reduce the number of dynamic alignment checks and the overhead incurred by them. Our method uses an interprocedural analysis which propagates pointer alignment information in function bodies and through function calls. The effectiveness of our method is supported by experimental results which show that in typical programs the alignments of about 50% of the pointers can be statically determined. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".