{"id":181639,"date":"2026-05-21T18:25:05","date_gmt":"2026-05-21T16:25:05","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/fluidx3d-v3-7"},"modified":"2026-05-21T18:25:05","modified_gmt":"2026-05-21T16:25:05","slug":"fluidx3d-v3-7","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/news\/fluidx3d-v3-7","title":{"rendered":"FluidX3D v3.7","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

<\/p>\n

FluidX3D 3.7<\/a> – pacote para din\u00e2mica de fluidos computacional, utilizando o m\u00e9todo de equa\u00e7\u00f5es de Boltzmann em grade (LBM) e OpenCL. O projeto \u00e9 posicionado como um pacote CFD de alto desempenho e econ\u00f4mico em termos de mem\u00f3ria, capaz de operar em GPUs e CPUs de diferentes fabricantes atrav\u00e9s do OpenCL.<\/p>\n

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

A principal mudan\u00e7a na vers\u00e3o 3.7 foi a otimiza\u00e7\u00e3o do n\u00facleo de renderiza\u00e7\u00e3o da isopletura do crit\u00e9rio Q (graphics_q()), utilizada para visualizar estruturas vorticais no fluxo. Na implementa\u00e7\u00e3o anterior, o n\u00facleo carregava da mem\u00f3ria de v\u00eddeo um pesado template de 32 velocidades tridimensionais para cada c\u00e9lula da grade. Agora, um grupo de trabalho de threads de 8\u00d78\u00d78 carrega um bloco de 11\u00d711\u00d711 velocidades na mem\u00f3ria local de primeiro n\u00edvel de 16 KB, ap\u00f3s o qual threads adjacentes reutilizam esses dados. De acordo com o autor, isso reduz o n\u00famero de carregamentos da VRAM em aproximadamente 12 vezes \u2014 para 2,6 velocidades por c\u00e9lula \u2014 e move o n\u00facleo de um modo limitado pela largura de banda da mem\u00f3ria para um modo limitado por c\u00e1lculos.<\/p>\n

O ganho pr\u00e1tico depende do hardware espec\u00edfico. Em novas GPUs com sistema de cache avan\u00e7ado, a otimiza\u00e7\u00e3o manual da mem\u00f3ria local pode quase n\u00e3o trazer ganhos ou at\u00e9 reduzir ligeiramente a taxa de quadros. Em algumas GPUs mais antigas, o autor observou acelera\u00e7\u00f5es de at\u00e9 duas vezes, especialmente not\u00e1veis no modo gr\u00e1fico interativo do FluidX3D. Por exemplo, em testes publicados, a NVIDIA Titan Xp acelerou de 7 para 13 quadros\/s, a Intel UHD 770 de 8 para 14 quadros\/s em um dos modos, e a Intel Arc A750 de 22 para 26 quadros\/s.<\/p>\n

Para GPUs integradas mais antigas, h\u00e1 um retrocesso autom\u00e1tico para a implementa\u00e7\u00e3o anterior: parte desses dispositivos n\u00e3o suporta grupos de trabalho com 512 threads ou possui menos de 16 KB de mem\u00f3ria local L1. Nesses casos, o FluidX3D continuar\u00e1 a usar a vers\u00e3o antiga do n\u00facleo, mantendo a compatibilidade.<\/p>\n

Nesta vers\u00e3o, tamb\u00e9m foram simplificadas as condi\u00e7\u00f5es para renderiza\u00e7\u00e3o em VR, a desdobramento de loops na rasteriza\u00e7\u00e3o foi desativado para reduzir o \"spaghetti\" na sa\u00edda de montagem, micro-otimiza\u00e7\u00f5es foram adicionadas em convert_triangle\/_interpolated(), e no adaptador OpenCL foi corrigida a sa\u00edda de erro ao nome do n\u00facleo estar vazio e ao tamanho do grupo de trabalho.<\/p>\n

<\/p>\n

FluidX3D inclui um renderizador 3D interativo embutido, que permite visualizar campos de velocidade, linhas de corrente, is superf\u00edcies do crit\u00e9rio Q e cortes durante a simula\u00e7\u00e3o, sem a necessidade de exporta\u00e7\u00e3o para uma ferramenta de p\u00f3s-processamento separada. FluidX3D<\/a> O projeto tamb\u00e9m destaca o suporte a superf\u00edcies livres, convec\u00e7\u00e3o t\u00e9rmica, modelo de turbul\u00eancia Smagorinsky-Lilly, rastreamento de part\u00edculas, exporta\u00e7\u00e3o VTK e opera\u00e7\u00e3o com v\u00e1rias GPUs.<\/p>\n

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

\u00c9 importante mencionar o status de licen\u00e7a: o c\u00f3digo-fonte do FluidX3D \u00e9 aberto e dispon\u00edvel no GitHub; no entanto, a licen\u00e7a do projeto<\/a> n\u00e3o \u00e9 livre no sentido estrito da OSI\/FSF. Ela permite o uso para pesquisas p\u00fablicas, educa\u00e7\u00e3o e fins pessoais, mas pro\u00edbe a aplica\u00e7\u00e3o comercial e militar, assim como o treinamento de modelos de IA com o c\u00f3digo-fonte.<\/p>\n

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

Fonte: linux.org.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

FluidX3D 3.7 – \u043f\u0430\u043a\u0435\u0442 \u0434\u043b\u044f \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u0433\u0438\u0434\u0440\u043e\u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0438, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0449\u0438\u0439 \u043c\u0435\u0442\u043e\u0434 \u0440\u0435\u0448\u0451\u0442\u043e\u0447\u043d\u044b\u0445 \u0443\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0439 \u0411\u043e\u043b\u044c\u0446\u043c\u0430\u043d\u0430 (LBM) \u0438 OpenCL. \u041f\u0440\u043e\u0435\u043a\u0442 \u043f\u043e\u0437\u0438\u0446\u0438\u043e\u043d\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0432\u044b\u0441\u043e\u043a\u043e\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0438 \u044d\u043a\u043e\u043d\u043e\u043c\u043d\u044b\u0439 \u043f\u043e \u043f\u0430\u043c\u044f\u0442\u0438 CFD-\u043f\u0430\u043a\u0435\u0442, \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u044b\u0439 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u043d\u0430 GPU \u0438 CPU \u0440\u0430\u0437\u043d\u044b\u0445 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u0435\u0439 \u0447\u0435\u0440\u0435\u0437 OpenCL. \u0413\u043b\u0430\u0432\u043d\u044b\u043c \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u0432\u0435\u0440\u0441\u0438\u0438 3.7 \u0441\u0442\u0430\u043b\u0430 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u044f\u0434\u0440\u0430 \u043e\u0442\u0440\u0438\u0441\u043e\u0432\u043a\u0438 \u0438\u0437\u043e\u043f\u043e\u0432\u0435\u0440\u0445\u043d\u043e\u0441\u0442\u0438 Q-\u043a\u0440\u0438\u0442\u0435\u0440\u0438\u044f (graphics_q()), \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u043e\u0439 \u0434\u043b\u044f \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0432\u0438\u0445\u0440\u0435\u0432\u044b\u0445 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440 \u0432 \u043f\u043e\u0442\u043e\u043a\u0435. \u0412 \u043f\u0440\u0435\u0436\u043d\u0435\u0439 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":8,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-181639","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47FluidX3D v3.7 | ProHoster","description":"FluidX3D 3.7 \u2013 um pacote para din\u00e2mica de fluidos computacional, utilizando o m\u00e9todo de equa\u00e7\u00f5es de Boltzmann em grade (LBM) e 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\u0440\u0435\u0448\u0451\u0442\u043e\u0447\u043d\u044b\u0445 \u0443\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0439 \u0411\u043e\u043b\u044c\u0446\u043c\u0430\u043d\u0430 (LBM) \u0438 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