Low overhead dynamic binary translation on ARM
Bibliographic record
Abstract
The ARMv8 architecture introduced AArch64, a 64-bit execution mode with a new instruction set, while retaining binary compatibility with previous versions of the ARM architecture through AArch32, a 32-bit execution mode. Most hardware implementations of ARMv8 processors support both AArch32 and AArch64, which comes at a cost in hardware complexity. We present MAMBO-X64, a dynamic binary translator for Linux which executes 32-bit ARM binaries using only the AArch64 instruction set. We have evaluated the performance of MAMBO-X64 on three existing ARMv8 processors which support both AArch32 and AArch64 instruction sets. The performance was measured by comparing the running time of 32-bit benchmarks running under MAMBO-X64 with the same benchmark running natively. On SPEC CPU2006, we achieve a geometric mean overhead of less than 7.5% on in-order Cortex-A53 processors and a performance improvement of 1% on out-of-order X-Gene 1 processors. MAMBO-X64 achieves such low overhead by novel optimizations to map AArch32 floating-point registers to AArch64 registers dynamically, handle overflowing address calculations efficiently, generate traces that harness hardware return address prediction, and handle operating system signals accurately.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".