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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".