Optimizing shared data accesses in distributed-memory X10 systems
Bibliographic record
Abstract
Prior studies have established the performance impact of coherence protocols optimized for specific patterns of shared-data accesses in Non-Uniform-Memory-Architecture (NUMA) systems. First, this work incorporates a directory-based protocol into the runtime system of X10 — a Partitioned-Global-Address-Space (PGAS) programming language — to manage read-mostly, producer-consumer, stencil, and migratory variables. This protocol complements the existing X10Protocol, which keeps a unique copy of a shared variable and relies on message transfers for all remote accesses. The X10Protocol is effective to manage accumulator, write-mostly and general read-write variables. Then, it introduces a new shared-variable access-pattern profiler that is used by a new coherence-policy manager to decide which protocol should be used for each shared variable. The profiler can be run in both offline and online modes. An evaluation on a 128-core distributed-memory machine reveals that coordination between these protocols does not degrade performance on any of the applications studied, and achieves speedup in the range of 15% to 40% over X10Protocol. The performance is also comparable to carefully hand-written versions of the applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".