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 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".