Bibliographic record
Abstract
A recent study presented an interesting statistic that about one-third and up to two-thirds of the load instructions in many x86 programs are redundant. These redundant loads fetch the same value from the same address throughout the execution of the program, and are called global stable loads (GSLs). We explore the behavior of GSLs across different architectures, compilers, and compiler optimizations. We also study GSLs in emerging ML workloads that are not explored in prior work. Our characterization of SPEC programs reveal a high locality in GSLs, that only 4.2-5.2% of static GSLs are responsible for 90-95% of the dynamic GSLs. We identify the reasons behind these dominant static GSLs, and posit that many of these GSLs are identifiable by the compiler prior to execution.In contrast to using a purely microarchitectural solution to eliminate GSLs, we consider a compiler-hardware co-designed solution wherein the compiler marks the likely stable loads, and the microarchitecture uses a small and fast cache to service these loads quickly and efficiently. We achieve a maximum speedup up to 7.43% for SPEC Float programs, and attain an energy reduction of 12.95% for the L1-D cache. We achieve these results using minimal hardware and without the need for a complex microarchitectural mechanism, yet the results are similar to state-of-the-art solutions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".