Inside the FFT Black Box: Serial and Parallel Fast Fourier Transform Algorithms
Bibliographic record
Abstract
PRELIMINARIES An Elementary Introduction to the Discrete Fourier Transform Some Mathematical and Computational Preliminaries SEQUENTIAL FFT ALGORITHMS The Divide-and-Conquer Paradigm and Two Basic FFT Algorithms Deciphering the Scrambled Output from In-Place FFT Computation Bit-Reversed Input to the Radix-2 DIF FFT Performing Bit-Reversal by Repeated Permutation of Intermediate Results An In-Place Radix-2 DIT FFT for Input in Natural Order An In-Place Radix-2 DIT FFT for Input in Bit-Reversed Order An Ordered Radix-2 DIT FFT Ordering Algorithms and Computer Implementation of Radix-2 FFTs The Radix-4 and the Class of Radix-2s FFTs The Mixed-Radix and Split-Radix FFTs FFTs for Arbitrary N FFTs for Real Input FFTs for Composite N Selected FFT Applications PARALLEL FFT ALGORITHMS Parallelizing the FFTs: Preliminaries on Data Mapping Computing and Communications on Distributed-Memory Multiprocessors Parallel FFTs without Inter-Processor Permutations Parallel FFTs with Inter-Processor Permutations A Potpourri of Variations on Parallel FFTs Further Improvement and a Generalization of Parallel FFTs Parallelizing Two-Dimensional FFTs Computing and Distributing Twiddle Factors in the Parallel FFTs APPENDICES Fundamental Concepts of Efficient Scientific Computation Solving Recurrence Equations by Substitution Bibliography
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.047 |
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".