Visualizing complex dynamics in many-core accelerator architectures
Why this work is in the frame
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Bibliographic record
Abstract
While many-core accelerator architectures, such as today's Graphics Processing Units (GPUs), offer orders of magnitude more raw computing power than contemporary CPUs, their massive parallelism often produces complex dynamic behaviors even with the simplest applications. Using a fixed set of hardware or simulator performance counters to quantify behavior over a large interval of time such as an entire application execution run or program phase may not capture this behavior. Software and/or hardware designers may consequently miss out on opportunities to optimize for better performance. Similarly, significant effort may be expended to find metrics that explain anomalous behavior in architecture design studies. Moreover, the increasing complexity of applications developed for today's GPU has created additional difficulties for software developers when attempting to identify bottlenecks of an application for optimization. This paper presents a novel GPU performance visualization tool, AerialVision, to address these two problems. It interfaces with the GPGPU-Sim simulator to capture and visualize the dynamic behavior of a GPU architecture throughout an application run. Similar to existing performance analysis tools for CPUs, it can annotate individual lines of source code with performance statistics to simplify the bottleneck identification process. To provide further insight, AerialVision introduces a novel methodology to relate pathological dynamic architectural behaviors resulting in performance loss with the part of the source code that is responsible. By rapidly providing insight into complex dynamic behavior, AerialVision enables research on improving many-core accelerator architectures and will help ensure applications written for these architectures reach their full performance potential.
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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