Design Space Exploration of Embedded Applications on Heterogeneous CPU-GPU Platforms
Bibliographic record
Abstract
CPU-GPU platforms possess the potential of enhancing the performance of applications through some unique and diverse capabilities of both CPU-GPU devices. As a result, the methodologies for CPU/GPU system design space exploration for various applications are now considerably more challenging on these heterogeneous platforms. In this paper, we present a heuristic algorithm for partitioning the computation of applications between a CPU and GPU, while satisfying the user-defined constraints. Our methodology leverages the SIMD-related computing and hierarchical memory model of GPUs to optimize application mapping and allocation to CPU-GPU systems. The algorithm partitions the application, which is specified as a Directed Acyclic Graph (DAG), for a CPU-GPU platform to meet the objectives specified by the user. The effectiveness of our methodology is demonstrated by efficiently partitioning and executing MJPEG decoder and benchmark applications on a CPU-GPU system.
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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 | 0.000 |
| 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".