Using combined profiling to decide when thread level speculation is profitable
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thread Level Speculation (TLS) speculatively executes parts of a program in parallel. Statically determined may dependences between store-load pairs prevent the compiler from speculatively executing parts of programs (e.g loop iterations or functions). If a compiler can determine that the probability of a may dependence occurring at runtime is low, then it can use TLS to execute the loop in parallel. This research will develop a may dependence profiling framework that is able to capture the effect of different inputs on the dependence behaviour of the program during runtime, using a technique called Combined Profiling (CP) [1]. The dependence profiling will be made efficient using the output from static analysis. TLS code generation strategies will be implemented in a version of the LLVM compiler that will generate code for the hardware support for TLS in the IBM BlueGene/Q (BG/Q) machine.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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