{"id":80460,"date":"2026-03-16T15:53:03","date_gmt":"2026-03-16T14:53:03","guid":{"rendered":"https:\/\/www.he-arc.ch\/?post_type=he-arc_work&p=80460"},"modified":"2026-03-18T10:27:46","modified_gmt":"2026-03-18T09:27:46","slug":"yerly-sevan","status":"publish","type":"he-arc_work","link":"https:\/\/www.he-arc.ch\/en\/projets-etudiants\/yerly-sevan\/","title":{"rendered":"YERLY Sevan"},"content":{"rendered":"\n

L\u2019objectif de ce travail de bachelor \u00e9tait d\u2019impl\u00e9menter le calcul d\u2019un facteur appel\u00e9 Lambda Happy, utilis\u00e9 pour r\u00e9soudre des mod\u00e8les lin\u00e9aires sparse. Ce facteur agit comme un poids de p\u00e9nalisation sur les coefficients non nuls, for\u00e7ant le mod\u00e8le \u00e0 ne conserver que les variables les plus pertinentes.<\/p>\n\n\n\n

Le principal d\u00e9fi r\u00e9sidait dans le temps de calcul \u00e9lev\u00e9 du Lambda Happy, n\u00e9cessitant des optimisations. L\u2019algorithme devait \u00eatre int\u00e9gr\u00e9 dans Python via un package d\u00e9di\u00e9.<\/p>\n\n\n\n

Deux impl\u00e9mentations principales ont \u00e9t\u00e9 r\u00e9alis\u00e9es pour acc\u00e9l\u00e9rer le calcul : la premi\u00e8re avec PyTorch, un framework Python pour le calcul sur tenseurs, et la seconde avec des kernels et biblioth\u00e8ques CUDA, une plateforme de calcul parall\u00e8le d\u00e9velopp\u00e9e par NVIDIA.<\/p>\n\n\n\n

C\u2019est cette derni\u00e8re, combin\u00e9e \u00e0 un algorithme multi-GPU, qui a donn\u00e9 les meilleurs r\u00e9sultats.<\/p>\n\n\n\n

Un binding Python-C++ a \u00e9galement \u00e9t\u00e9 effectu\u00e9 pour int\u00e9grer l\u2019impl\u00e9mentation dans un package Python. Le package a \u00e9t\u00e9 publi\u00e9 et peut \u00eatre install\u00e9 sous Linux.<\/p>\n\n\n\n

Les r\u00e9sultats obtenus sont prometteurs, passant de 0,4 FPS sur la premi\u00e8re version \u00e0 plus de 2\u2032104 FPS, soit un facteur d\u2019am\u00e9lioration de 5\u2032260. Ces performances sont bas\u00e9es sur des matrices de taille fixe, dont un dataset de dimension 1000 par 1000, et peuvent varier selon le mat\u00e9riel utilis\u00e9.<\/p>\n\n\n\n

Ce facteur peut aussi \u00eatre utilis\u00e9 dans les r\u00e9seaux de neurones sparse. Des algorithmes ont d\u00e9j\u00e0 \u00e9t\u00e9 propos\u00e9s et pourraient constituer une continuation de ce travail.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Poster du travail de bachelor de Sevan Yerly<\/a><\/p>\n\n\n\n

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