Enhancing operating system support for multicore processors by using hardware performance monitoring
Why this work is in the frame
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Bibliographic record
Abstract
Multicore processors contain new hardware characteristics that are different from previous generation single-core systems or traditional SMP (symmetric multiprocessing) multiprocessor systems. These new characteristics provide new performance opportunities and challenges. In this paper, we show how hardware performance monitors can be used to provide a fine-grained, closely-coupled feedback loop to dynamic optimizations done by a multicore-aware operating system. These multicore optimizations are possible due to the advanced capabilities of hardware performance monitoring units currently found in commodity processors, such as execution pipeline stall breakdown and data address sampling. We demonstrate three case studies on how a multicore-aware operating system can use these online capabilities for (1) determining cache partition sizes, which helps reduce contention in the shared cache among applications, (2) detecting memory regions with bad cache usage, which helps in isolating these regions to reduce cache pollution, and (3) detecting sharing among threads, which helps in clustering threads to improve locality. Using realistic applications from standard benchmark suites, the following performance improvements were achieved: (1) up to 27% improvement in IPC (instructions-per-cycle) due to cache partition sizing; (2) up to 10% reduction in cache miss rates due to reduced cache pollution, resulting in up to 7% improvement in IPC; and (3) up to 70% reduction in remote cache accesses due to thread clustering, resulting in up to 7% application-level improvement.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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