Dynamic Memory Bandwidth Allocation for Real-Time GPU-Based SoC Platforms
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Bibliographic record
Abstract
Heterogeneous SoC platforms, comprising both general purpose CPUs and accelerators, such as a GPU, are becoming increasingly attractive for real-time and mixed-criticality systems to cope with the computational demand of data parallel applications. However, contention for access to shared main memory can lead to significant performance degradation on both CPU and GPU. Existing work has shown that memory bandwidth throttling is effective in protecting real-time applications from memory-intensive, best-effort (BE) ones; however, due to the inherent pessimism involved in worst-case execution time (WCET) estimation, such approaches can unduly restrict the bandwidth available to BE applications. In this article, we propose a novel memory bandwidth allocation scheme where we dynamically monitor the progress of a real-time application and increase the bandwidth share of BE ones whenever it is safe to do so. Specifically, we demonstrate our approach by protecting a real-time GPU kernel from BE CPU tasks. Based on profiling information, we first build a WCET estimation model for the GPU kernel. Using such model, we then show how to dynamically recompute on-line the maximum memory budget that can be allocated to BE tasks without exceeding the kernel's assigned execution budget. We implement our proposed technique on NVIDIA embedded SoC and demonstrate its effectiveness on a variety of GPU and CPU benchmarks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it