LTTng‐HSA: Bringing LTTng tracing to HSA‐based GPU runtimes
Bibliographic record
Abstract
Summary In this paper, we propose LTTng‐HSA, a set of tools that allow for the collection of a single, unified software graphics processing unit (GPU) trace in ROCr, a Heterogeneous System Architecture (HSA)‐based API and runtime. HSA is a cross‐vendor standard facilitating the programming of heterogeneous systems that include CPUs, GPUs, and possibly other types of devices. Our open‐source solution is generic and easily adaptable to diverse GPU runtimes or APIs. Using Linux Trace Toolkit Next Generation (LTTng), a highly efficient Linux tracer, it collects different types of events over multiple executions of an application and aims to gather all the data into a single trace, offering an easy way to generate GPU‐related traces. Our instrumentation is achieved simply by preloading libraries, without recompiling the target application, which makes it flexible and easy to use. The resulting traces, which include API call stack information, GPU hardware metrics, command queue, and compute kernel profiling, are well adapted for postprocessing and further analysis. Our solution also includes tracing data from the Linux kernel and proposes views for Trace Compass, an interactive trace visualizer.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".