Bibliographic record
Abstract
As a standard C++ programming model, SYCL has gained popularity on incorporating various parallel computing frameworks.With the development of hardware technologies, low-level computing devices are becoming increasingly varied and thus result in the great heterogeneity of hardware.Although many computing frameworks, such as OpenCL, OpenMP and CUDA, can benefit to heterogeneous computing, they increase the complexity of cross-platform deployment and reduce productivity due to low portability and miscellaneous features.By comparison, SYCL allows programmers to write high-performance parallel applications in the standard C++ syntax and execute them across vendor-specific hardware without diving into low-level technologies.However, despite the popularity of SYCL on Windows and Linux, there is little research on porting SYCL to QNX, a real-time operating system (RTOS).Therefore, we choose two SYCL implementations and conduct corresponding experiments.In particular, we build a new path of calling OpenCL APIs in SYCL-GTX and significantly reduce the time of compiling SYCL kernels.Although the overall performance of SYCL-GTX on QNX is evaluated on Linux, our experiments demonstrate that there are many possible optimizations that can improve SYCL-GTX on QNX.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".