Adapting SYCL’s SIMT Programming Paradigm for Accelerators via Program Reconstruction
Bibliographic record
Abstract
We present an IR-to-IR Converter that is capable of converting from LLVM IR to Halide IR and MLIR’s Affine Dialect to support generation of high performance SYCL kernel code [10] targeting accelerators with explicit memory hierarchy design. The converter performs program reconstruction to raise abstraction of the IR. Once the IR is raised to the level of Halide IR, AKG [2] can be leveraged to generate performant DaVinci code [2]. Alternatively, when the IR is raised to MLIR’s Affine Dialect, existing MLIR Affine passes with minor modifications can be readily used. Subsequently, the IR is lowered back to LLVM IR through progressive lowering. LLVM’s LLC is used to create the final binary for both cases. We extend upon SYCL’s buffer, accessor and parallel_for abstractions to facilitate the IR raising process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".