XRT: Programming-Language Independent MapReduce on Shared-Memory Systems
Bibliographic record
Abstract
Increasing processor core-counts have created an opportunity for efficient parallel processing of large datasets on shared-memory systems. When compared to clusters of networked commodity hardware, shared-memory systems have the potential to provide better per-core performance, a more straightforward development environment and reduced operational overhead. This paper presents XRT, a high-performance and programming-language independent MapReduce runtime for shared-memory systems. XRT is built to be simple to use, pedantic about resource usage and capable of utilizing disk-based data structures for processing datasets too large to fit in memory. To our knowledge, XRT is the first MapReduce runtime explicitly designed for programming-language independent MapReduce. Moreover, XRT is the first MapReduce runtime for shared-memory systems taking advantage of disk-based data structures for processing datasets which cannot fit in memory. Benchmarks of three common data processing problems demonstrate the disk-based capabilities as well as the excellent speedup profile of XRT as system core-counts increase.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".