Reducing Compiler-Inserted Instrumentation in Unified-Parallel-C Code Generation
Bibliographic record
Abstract
Programs written in Partitioned Global Address Space (PGAS) languages can access any location of the entire address space via standard read/write operations. However, the compiler have to create the communication mechanisms and the runtime system to use synchronization primitives to ensure the correct execution of the programs. However, PGAS programs may have fine-grained shared accesses that lead to performance degradation. One solution is to use the inspector-executor technique to determine which accesses are indeed remote and which accesses may be coalesced in larger remote access operations. A straightforward implementation of the inspector-executor in a PGAS system may result in excessive instrumentation that hinders performance. This paper introduces a shared-data localization transformation based on linear memory descriptors (LMADs) that reduces the amount of instrumentation introduced by the compiler into programs written in the UPC language and describes a prototype implementation of the proposed transformation. A performance evaluation, using up to 2048 cores of a POWER 775 supercomputer, allows for a prediction that applications with regular accesses can achieve up to 180% of the performance of handoptimized versions while applications with irregular accesses yield performance gain from 1.12X up to 6.3X speedup.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".