The 2-way thrashing-avoidance cache (TAC): an efficient instruction cache scheme for object-oriented languages
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Bibliographic record
Abstract
This paper presents a new instruction cache scheme: the TAC (Thrashing-Avoidance Cache). A 2-way TAC scheme employs 2-way banks and XOR mapping functions. The main function of the TAC is to place a group of instructions separated by a call instruction into a bank according to the Bank Selection Logic (BSL) and Bank-originated Pseudo-LRU replacement policies (BoPLRU). After the BSL initially selects a bank on an instruction cache miss, the BoPLRU will determine the final bank for updating a cache line as a correction mechanism. These two mechanisms can guarantee that recent groups of instructions exist in each bank safely. We have developed a simulation program, TACSim, by using Shade and Spixtools, provided by SUN Microsystems, on an ultra SPARC/10 processor. Our experimental results show that 2-way TAC schemes reduce conflict misses more effectively than 2-way skewed-associative caches in both C (17% improvement) and C++ (30% improvement) programs on L1 caches.
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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 | 0.001 | 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 | 0.001 | 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