Applying Models of Computation to OpenCL Pipes for FPGA Computing
Bibliographic record
Abstract
OpenCL pipes offer a powerful construct for synthesizing multi-kernel FPGA applications with inter-kernel communication dependencies. The communication discipline between the FPGA kernels is restricted to producer-consumer style patterns supported with on-chip FPGA FIFOs. While this provides few restrictions on the usage, the OpenCL compiler is unable to provide guarantees on buffering capacity or schedulability of the connected kernels. Without these guarantees, an OpenCL developer may over-provision hardware resources or assume pessimistic timing during scheduling. We propose imposing a communication discipline inspired from models of computation (e.g.Ptolemy) such as synchronous dataflow (SDF), and bulk synchronous (BSP). These models offer a restricted subset of communication patterns that enable implementation tradeoffs and deliver performance and resource guarantees. This is useful for OpenCL developers operating within the constraints of the FPGA device. We provide a preliminary analysis of our proposal and sketch programmer and compiler responsibilities that would be needed for integrating these features into the FPGA OpenCL environment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".