Data-dependence profiling to enable safe thread level speculation
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
Data-dependence profiling is a technique that enables a compiler to judiciously decide when the execution of a loop --- which the compiler could not prove to be dependence free --- should be speculated through the use of Thread Level Speculation (TLS). The data collected by a data-dependence profiler can be used to predict if may dependencies reported by a compiler static analysis are likely to materialize at runtime. A cost analysis can then be used to decide that some loops with a lower probability of dependence should be speculatively parallelized. This paper addresses the question as to whether a loops' dependence behaviour changes when the input to the program changes --- a study of 57 different benchmarks indicates that it usually does not change. Then the paper describes SpecEval, an automatic speculative parallelization framework that uses single-input data-dependence profiles to find speculation candidates in the SPEC2006 and PolyBench/C benchmarks. This paper also presents a performance evaluation of TLS implementation in IBM's Blue-Gene/Q supercomputer and shows that the performance of TLS is affected by several factors, including the number of speculated loops, the execution-time coverage of speculated loops, the miss-speculation overhead, the L1 cache miss rate and the effect on dynamic instruction path length.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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