icXML: Accelerating a Commercial XML Parser Using SIMD and Multicore Technologies
Bibliographic record
Abstract
Prior research on the acceleration of XML processing using single-instruction multiple-data (SIMD) and multi-core parallelism has lead to a number of interesting research prototypes. This work is the first to investigate to the extent to which the techniques underlying these prototypes could result in systematic performance benefits when fully integrated into a commercial XML parser The widely used Xerces-C++ parser of the Apache Software Foundation was chosen as the foundation for the study. A systematic restructuring of the parser was undertaken, while maintaining the existing API for application programmers. Using SIMD techniques alone, an increase in parsing speed of at least 50% was observed in a range of applications. When coupled with pipeline parallelism on dual core processors, improvements of 2x and beyond were realized. icXML is intended as an important industrial contribution in its own right as well as an important case study for the underlying Parabix parallel processing framework. Based on the success of the icXML development, there is a strong case for continued development of that framework as well as for the application of that framework to other important XML technology stacks. An important area for further work is the extension of Parabix technology to accelerate Java-based implementations as well as ones based on C/C++.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".