A first-order fine-grained multithreaded throughput model
Why this work is in the frame
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Bibliographic record
Abstract
Analytical modeling is an alternative to detailed performance simulation with the potential to shorten the development cycle and provide additional insights. This paper proposes analytical models for predicting the cache contention and throughput of heavily multithreaded architectures such as Sun Microsystems' Niagara. First, it proposes a novel probabilistic model to accurately predict the number of extra cache misses due to cache contention for significantly larger numbers of threads than possible with prior analytical cache contention models. Then it presents a Markov chain model for analytically estimating the throughput of multicore, fine-grained multithreaded architectures. The Markov model uses the number of stalled threads as the states and calculates transition probabilities based upon the rates and latencies of events stalling a thread. By modeling the overlapping of the stalls among threads and taking account of cache contention our models accurately predict system throughput obtained from a cycle-accurate performance simulator with an average error of 7.9%. We also demonstrate the application of our model to a design problem-optimizing the design of fine-grained multithreaded chip multiprocessors for application-specific workloads-yielding the same result as detailed simulations 65 times faster. Moreover, this paper shows that our models accurately predict cache contention and throughput trends across varying workloads on real hardware-a Sun Fire T1000 server.
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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 | 0.000 | 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 | 0.000 |
| 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 it