Taking Back Control in an Intermediate Representation for GPU Computing
Bibliographic record
Abstract
We describe our experiences successfully applying lightweight formal methods to substantially improve and reformulate an important part of Standard Portable Intermediate Representation SPIRV, an industry-standard language for GPU computing. The formal model that we present has allowed us to (1) identify several ambiguities and needless complexities in the way that structured control flow was defined in the SPIRV specification; (2) interact with the authors of the SPIRV specification to rectify these problems; (3) validate the developer tools and conformance test suites that support the SPIRV language by cross-checking them against our formal model, improving the tools, test suites, and our models in the process; and (4) develop a novel method for fuzzing SPIRV compilers to detect miscompilation bugs that leverages our formal model. The latest release of the SPIRV specification incorporates the revised set of control-flow definitions that have arisen from our work. Furthermore, our novel compiler-fuzzing technique has led to the discovery of twenty distinct, previously unknown bugs in SPIRV compilers from Google, the Khronos Group, Intel, and Mozilla. Our work showcases the practical impact that formal modelling and analysis techniques can have on the design and implementation of industry-standard programming languages.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".