Exploring Various Levels of Parallelism in High-Performance CRC Algorithms
Bibliographic record
Abstract
Modern processors have increased the capabilities of instruction-level parallelism (ILP) and thread-level parallelism (TLP). These resources, however, typically exhibit poor utilization on conventional cyclic redundancy check (CRC) algorithms. In this paper, various levels of parallelism in high-performance CRC algorithms are investigated. The main idea of the proposed algorithms is to make full utilization of modern processors, from the perspective of both instruction-level and thread-level parallelism. First, a fine-grained algorithm executes the CRC computation in an interleaved manner, so that multiple independent data flows can be processed simultaneously. This algorithm allows instruction-level parallelism, which triples and doubles the performance of the existing slicing-by-4 and slicing-by-8 algorithms, respectively. Second, a coarse-grained algorithm can ideally deal with data in a parallel way by parallelizing a family of serial CRC generating algorithms. Therefore, this algorithm allows thread-level parallelism, which can make full use of multi-core computing capability. As a result, it achieves a speedup that is almost equal to the number of threads used. In addition, both fine-grained and coarse-grained algorithms can be applied together to achieve high throughput further. (This is an extended version of a paper that appeared at the 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) in Montreal, QC, Canada, in 2017.).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".