Performance impacts and limitations of hardware memory access trace collection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In today's multicore architectures, complex interactions between applications in the memory system can have a significant and highly variable impact on application execution time. System designers typically use hardware counters to profile execution behaviours and diagnose performance problems. However, hardware counters are not always sufficient and some problems are best identified with full memory access traces. Collecting these traces in software is very expensive; our work explores using dedicated hardware for memory-access trace collection. We analyze the limitations of this approach and its impacts on application performance. Our study is performed on actual hardware using two very different CPU platforms: 1) the PolyBlaze multicore soft processor and 2) the ARM Cortex-A9. In both cases, the data collection is implemented on an FPGA. Using micro-benchmarks designed to test the bounds of memory access behaviour, we illustrate the operational regions of data collection and the impact on system performance. By examining the bandwidth bottlenecks that limit the rate of data collection, as well as hardware architecture choices that can aggravate the impact on application performance, we provide guidelines that can be used to extrapolate our analysis to other systems and processor architectures.
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.
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 | 0.001 |
| 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