How useful are non-blocking loads, stream buffers and speculative execution in multiple issue processors?
Bibliographic record
Abstract
We investigate the relative performance impact of non-blocking loads, stream buffers, and speculative execution both used individually and in conjunction with each other. We have simulated the SPEC92 benchmarks on a statically scheduled quad-issue processor model, running code from the Multiflow compiler. Non-blocking loads and stream buffers both provide a significant performance advantage, and their combination performs significantly better than either alone. For example, with a 64-byte, 2-way set associative cache with 32 cycle fetch latency, non-blocking loads reduce the run-time by 21% while stream-buffers reduce it by 26%, and the combined use of the two yields a 47% reduction. The addition of speculative execution further improves the performance of the systems that we have simulated, with or without non-blocking loads and stream buffers, by an additional 20% to 4O%. We expect that the use of all three of these techniques will be important in future generations of microprocessors.>
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".