{"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\/et\/blog\/news\/fluidx3d-v3-7","title":{"rendered":"FluidX3D v3.7","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

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FluidX3D 3.7<\/a> – paket arvutuslikule h\u00fcdrod\u00fcnaamikale, mis kasutab Boltzmanni v\u00f5rguv\u00f5rrandite (LBM) meetodit ja OpenCL-i. Projekt on positsioneeritud kui k\u00f5rge j\u00f5udlusega ja m\u00e4lu \u00f6konoomne CFD-paket, mis suudab t\u00f6\u00f6tada erinevate tootjate GPU-de ja CPU-dega l\u00e4bi OpenCL-i.<\/p>\n

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Versiooni 3.7 peamine muudatus on Q-kriteeriumi isopinna renderdamise tuuma optimeerimine (graphics_q()), mida kasutatakse vortikaalsete struktuuride visualiseerimiseks voos. Eelmises teostuses laadis tuum videom\u00e4lust raske 32 kolmem\u00f5\u00f5tmelise kiirusmustri iga ruutmeetri jaoks. N\u00fc\u00fcd laadib 8\u00d78\u00d78 l\u00f5imede t\u00f6\u00f6grupp 11\u00d711\u00d711 kiirusest 16 KB esmaklassilise kohaliku m\u00e4lu plokki, mille j\u00e4rel k\u00f5rvalised l\u00f5imede jagavad need andmed. Autori hinnangul v\u00e4hendab see VRAM-ist laadimiste arvu umbes 12 korda \u2014 2,6 kiiruseni ruutmeetri kohta \u2014 ja viib tuuma re\u017eiimi, mis on piiratud mitte m\u00e4lu l\u00e4bilaskev\u00f5ime, vaid arvutuste poolest.<\/p>\n

Praktiline kasu s\u00f5ltub konkreetsest riistvarast. Uutel GPU-del, millel on arenenud puhvri s\u00fcsteem, v\u00f5ib kohandamine kohaliku m\u00e4lu optimeerimise osas peaaegu mitte pakkuda kasvu v\u00f5i isegi veidi v\u00e4hendada kaadrisagedust. Osade vanade GPU-de puhul on autor t\u00e4heldanud kahe korda kiiruskasvu, mis on eriti m\u00e4rkimisv\u00e4\u00e4rne FluidX3D interaktiivses graafikare\u017eiimis. N\u00e4iteks avaldatud testides kiirus t\u00f5usis NVIDIA Titan Xp-lt 7 kaadrilt\/s 13 kaadrini\/s, Intel UHD 770-lt 8 kaadrilt\/s 14 kaadrini\/s \u00fches re\u017eiimis, ja Intel Arc A750-lt 22 kaadrilt\/s 26 kaadrini\/s.<\/p>\n

Vanade integreeritud GPU-de jaoks on ette n\u00e4htud automaatne tagasip\u00f6\u00f6rdumine varasema teostuse juurde: osa sellistest seadmetest ei toeta 512 l\u00f5ime suuruseid t\u00f6\u00f6gruppe v\u00f5i on v\u00e4hem kui 16 KB L1 kohaliku m\u00e4lu. Nendes juhtudel j\u00e4tkab FluidX3D vana tuumaversiooni kasutamist, s\u00e4ilitades \u00fchilduvuse.<\/p>\n

Ka selles versioonis on lihtsustatud VR-renderdamise tingimusi, keelatud ts\u00fcklite lahti t\u00f5mbamine rastereerimisel \u00abspagetide\u00bb v\u00e4hendamiseks assamblee v\u00e4ljundis, lisatud mikro-optimeerimine funktsioonis convert_triangle\/_interpolated(), ja OpenCL-adapteris on parandatud veateavituste v\u00e4ljund, kui tuuma nimi ja t\u00f6\u00f6grupi suurus on t\u00fchjad.<\/p>\n

<\/p>\n

FluidX3D sisaldab sisseehitatud interaktiivset 3D-renderdamist, mis v\u00f5imaldab kiiresti vaadata kiirusv\u00e4lju, voolujooni, Q-kriteeriumi isopindasid ja l\u00f5ikeid otse arvutamise ajal, ilma et oleks vajalik eksportimine eraldi t\u00f6\u00f6tlemist\u00f6\u00f6riista. FluidX3D<\/a> Projektis on samuti v\u00e4lja toodud vabapinna toetamine, soojuskonvektsioon, Smagorinsky-Lilly turbulentse mudel, osakeste j\u00e4lgimine, VTK eksportimine ning mitme GPU kasutamine.<\/p>\n

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\n<\/p>\n

Erakordselt pakub t\u00e4helepanu ka litsentsi staatus: FluidX3D l\u00e4htekood on avatud ja saadaval GitHubis, kuid projekti litsents<\/a> ei ole OSI\/FSF m\u00f5ttes rangelt vabaks. See lubab kasutada avalikes teadusuuringutes, hariduses ja isiklikel eesm\u00e4rkidel, aga keelab kaubandusliku ja s\u00f5jalise kasutamise, samuti AI mudelite koolitamise l\u00e4htekoodil.<\/p>\n

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Allikas: 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\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 on pakett arvutuslikuks h\u00fcdrod\u00fcnaamikaks, mis kasutab Boltzmanni v\u00f5rrandite (LBM) meetodit ja 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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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