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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".