Implémentation parallèle des FFTs sur des systèmes multicoeurs pour la couche physique du LTE 4G
Bibliographic record
Abstract
N> 2 18 et aussi MKL pour N> 2 19 • Ce gain en vitesse peut atteindre un gain de 4 fois plus rapide sur Xeon et de 2.5 sur Xeon-Phi pour N=2 29 par rapport à FFTW sur 16 threads.En comparaison à la FFTW sur un seul thread, un gain en vitesse de 20 et 25 fois plus rapide sur Xeon-Phi (MIC) et sur Xeon, respectivement.vi Remerciement Je voudrais remercier mes parents Ammar et Wided et mes sœurs Afef et Wafa pour leur aide, soutien et assistance durant toutes les étapes de ce modeste travail.Je tiens à remercier mes amis Ghassen et Hocine pour leurs précieux conseils, mon directeur de recherche Daniel Massicotte, mon codirecteur Yvon Savaria et tous ceux qui ont contribué à réaliser ce projet.Je tiens aussi à remercier la Société Canadienne de Microélectronique (CMC) et Calcul Québec pour leurs infrastructures, leurs formations spécialisées et leurs services de centre de calcul.Finalement, je tiens à remercier le Regroupement Stratégique en Microsystèmes du Québec (ReSMiQ) pour le soutien financier.Abstract-Fast Fourier Transform (FFT) is a key element for wireless applications based on the OFDM (Orthogonal Frequency Division Multiplexing) and chaUenging for implementing on processor multicores/many-cores.As an ex ample, the Long Term Evolution (L TE) protocol establishes a requirement for processing, whereby many independent FFTs must be calculated within a Iimited time slot.By using Intel Math Kernel Library (MKL), in our approach to Xeon phi, we managed to reduce the maximum execution time of many independent FFTs.We proposed an implementation on processors multi-cores/many-cores using OpenMP (Open Multi-processing) reducing the mean time latency to ]24 l's on native mode after 1300 l's with the omoad.This is a challenge for shared memory projects.This paper describes how this level of performance can be obtained with multi-core Intel i7, Xeon processors and a many-core Xeon Phi.The best results were obtained with the Xeon Phi, which outperformed the Xeon Sandy-Bridge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".