On GPU pass-through performance for cloud gaming: Experiments and analysis
Bibliographic record
Abstract
Cloud Gaming renders interactive gaming applications remotely in the cloud and streams the scenes back to the local console over the Internet. Virtualization plays a key role in modern cloud computing platforms, allowing multiple users and applications to share a physical machine while maintaining isolation and performance guarantees. Yet the Graphical Processing Unit (GPU), which advanced game engines heavily rely upon, is known to be difficult to virtualize. Recent advances have enabled virtual machines to directly access physical GPUs and exploit their hardware's acceleration. This paper presents a experimental study on the performance of real world gaming applications as well as ray-tracing applications with GPUs. Despite the fact that the VMs are accelerated with dedicated physical GPUs, we find that the gaming applications perform poorly when virtualized, as compared to non-virtualized bare-metal base-line. For example, experiments with the Unigine gaming benchmark run at 85 FPS on our bare-metal hardware, however, when the same benchmark is run within a Xen or KVM based virtual machine the performance drops to less than 51 FPS. In contrast, ray-tracing application fares much better. Our detailed performance analysis using hardware profiling on KVM further reveals the memory bottleneck in the pass through access, particularly for real-time gaming applications.
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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".