Evaluating shared virtual memory in an OpenCL framework for embedded systems on FPGAs
Bibliographic record
Abstract
There is now significant interest in OpenCL for FPGAs because it is the first time the FPGA vendors have provided a programming model and a computing platform with integrated high-level synthesis. OpenCL is intended for heterogenous platforms, not just FPGAs, and the standard continues to evolve. Recently, OpenCL has introduced Shared Virtual Memory (SVM) with the goal of simplifying the programming model by allowing hosts and devices to access the same memory space more easily. In this paper, we propose different approaches to implement SVM in an OpenCL framework built specifically to study OpenCL in the context of embedded applications running on FPGAs. We evaluate these different approaches and compare the trade-offs between an OpenCL framework with SVM support and without SVM support. Our results show that the approach that implements the virtual address to physical address translation with a dedicated Memory Management Unit (MMU) performs better than the other approaches. Our results also show that, for input sizes less than 1MB for a vector addition benchmark, the OpenCL framework with SVM support performs better than the OpenCL framework without SVM support until the SVM handling in the kernel starts to dominate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".