Evaluating memory system performance of a large scale NUMA multiprocessor
Bibliographic record
Abstract
The effectiveness of large scale computing depends to a great extent on the performance of the memory system. As shared memory multiprocessors grow in size, their memory hierarchy deepens, resulting in a design with non-uniform latencies. In this paper, we explore the implications of multi-valued memory latencies. In particular, we study the effect of a non-uniform traffic distribution on a hierarchical large scale NUMA multiprocessor named Hector. Memory analysis is of interest because memory is a frequent source of poor performance in large scale multiprocessors. We have developed an analytical model that includes the effects of increased contention for system resources, and the impact of the arbitration algorithm on the network traffic. Our analysis has been validated with a detailed simulator. Also, we have examined two techniques for reducing memory latency. We assess the potential performance gains from replication of data and investigate the improvement in memory utilization by allowing memory request buffering. Furthermore, we studied the sensitivity of the memory performance to changes in background traffic. We found that inter-station traffic has a significant performance effect.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".