Visualization of profiling and tracing in CPU‐GPU programs
Bibliographic record
Abstract
Summary As the complexity of the toolchain increases for heterogeneous CPU‐GPU systems, the needs for comprehensive tracing and debugging tools also grows. Heterogeneous platforms bring new possibilities but also new performance issues that are hard to detect. Some techniques that were used on CPU programs are now adapted to GPUs. However, there are some concepts specific to GPUs, like SIMD processing, and the effects of the close interactions between the CPUs and the GPUs, with shared virtual memory and user‐level queues. Multiple sources of data need to be extracted and correlated to obtain a more global view of the performance. In this article, we introduce a novel approach for measuring and visualizing performance defects inside CPU‐GPU programs by combining kernel events, compute kernel events, user API calls and memory transfers. We created two new views that combine this information, to help provide a global view. This framework uses the open source user queue system described in the HSA standard. It can easily be adapted to any user queue system for heterogeneous computing devices. We compare this framework with current existing tools and test it against the Rodinia benchmark. We look at how the execution behavior affects the tracing and profiling overhead and we use Trace Compass to visualize the resulting trace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".