The Inner Most Loop Iteration counter: a new dimension in branch history
Bibliographic record
Abstract
The most efficient branch predictors proposed in academic literature exploit both global branch history and local branch history. However, local history branch predictor components introduce major design challenges, particularly for the management of speculative histories. Therefore, most effective hardware designs use only global history components and very limited forms of local histories such as a loop predictor. The wormhole (WH) branch predictor was recently introduced to exploit branch outcome correlation in multidimen-sional loops. For some branches encapsulated in a multidi-mensional loop, their outcomes are correlated with those of the same branch in neighbor iterations, but in the previous outer loop iteration. Unfortunately, the practical implementation of the WH predictor is even more challenging than the implementation of local history predictors. In this paper, we introduce practical predictor components to exploit this branch outcome correlation in multidimen-sional loops: the IMLI-based predictor components. The iteration index of the inner most loop in an application can be efficiently monitored at instruction fetch time using the Inner Most Loop Iteration (IMLI) counter. The outcomes of some branches are strongly correlated with the value of this IMLI counter. A single PC+IMLI counter indexed table, the IMLI-SIC table, added to a neural component of any recent predictor (TAGE-based or perceptron-inspired) captures this correlation. Moreover, using the IMLI counter, one can efficiently manage the very long local histories of branches that are targeted by the WH predictor. A second IMLI-based component, IMLI-OH, allows for tracking the same set of hard-to-predict branches as WH. Managing the speculative states of the IMLI-based pre-dictor components is quite simple. Our experiments show that augmenting a state-of-the-art global history predictor with IMLI components outperforms previous state-of-the-art academic predictors leveraging local and global history at much lower hardware complexity (i.e., smaller storage budget , smaller number of tables and simpler management of speculative states).
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 | 0.010 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".