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.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".