Managing GPU Concurrency in Heterogeneous Architectures
Why this work is in the frame
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Bibliographic record
Abstract
Heterogeneous architectures consisting of general-purpose CPUs and throughput-optimized GPUs are projected to be the dominant computing platforms for many classes of applications. The design of such systems is more complex than that of homogeneous architectures because maximizing resource utilization while minimizing shared resource interference between CPU and GPU applications is difficult. We show that GPU applications tend to monopolize the shared hardware resources, such as memory and network, because of their high thread-level parallelism (TLP), and discuss the limitations of existing GPU-based concurrency management techniques when employed in heterogeneous systems. To solve this problem, we propose an integrated concurrency management strategy that modulates the TLP in GPUs to control the performance of both CPU and GPU applications. This mechanism considers both GPU core state and system-wide memory and network congestion information to dynamically decide on the level of GPU concurrency to maximize system performance. We propose and evaluate two schemes: one (CM-CPU) for boosting CPU performance in the presence of GPU interference, the other (CM-BAL) for improving both CPU and GPU performance in a balanced manner and thus overall system performance. Our evaluations show that the first scheme improves average CPU performance by 24%, while reducing average GPU performance by 11%. The second scheme provides 7% average performance improvement for both CPU and GPU applications. We also show that our solution allows the user to control performance trade-offs between CPUs and GPUs.
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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 | 0.000 | 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 | 0.001 | 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