Dynamic Warp Formation and Scheduling for Efficient GPU Control Flow
Bibliographic record
Abstract
Recent advances in graphics processing units (GPUs) have resulted in massively parallel hardware that is easily programmable and widely available in commodity desktop computer systems. GPUs typically use single-instruction, multiple-data (SIMD) pipelines to achieve high performance with minimal overhead incurred by control hardware. Scalar threads are grouped together into SIMD batches, sometimes referred to as warps. While SIMD is ideally suited for simple programs, recent GPUs include control flow instructions in the GPU instruction set architecture and programs using these instructions may experience reduced performance due to the way branch execution is supported by hardware. One approach is to add a stack to allow different SIMD processing elements to execute distinct program paths after a branch instruction. The occurrence of diverging branch outcomes for different processing elements significantly degrades performance. In this paper, we explore mechanisms for more efficient SIMD branch execution on GPUs. We show that a realistic hardware implementation that dynamically regroups threads into new warps on the fly following the occurrence of diverging branch outcomes improves performance by an average of 20.7% for an estimated area increase of 4.7%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".