Uhlolojikelele
I-Tensor Cores amayunithi ehadiwe akhethekile ngaphakathi kwe-NVIDIA GPU yesimanje enza imisebenzi ye-matrix yokuphindaphinda nokuqongelela ngokushesha okukhulu. Kuyisizathu esiyinhloko sokuthi i-GPU eyodwa ingaqeqesha futhi isebenzise ama-oda amakhulu wenethiwekhi ye-neural yobukhulu ngokushesha kunokuba ikhompuyutha yenjongo evamile ingavumela.
I-Tensor Cores iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini.
I-Deep Dive
Eyethulwe ngesakhiwo se-Volta ngo-2017, i-Tensor Cores iyisekhethi ezinikezele ehlanganisa ukuphindaphinda okuncane kwe-matrix kanye nokwengezwa (D = A x B + C) ekusebenzeni okukodwa, kunokwenza ukuphindaphinda ngakunye ngesikhathi kuma-cores we-CUDA ajwayelekile. Ngoba cishe yonke ingqimba yenethiwekhi ye-neural yehlisela ekuphindaphindeni kwe-matrix, lokhu kufana nezibalo ezidingwa yi-AI. Isizukulwane ngasinye se-GPU sandise esikuphathayo: I-Volta yenza amathayela angu-4x4 FP16, kwathi kamuva izakhiwo ze-Ampere, Hopper, ne-Blackwell zengeza amafomethi anemba eliphansi njenge-TF32, BF16, INT8, FP8, ne-FP4. Ukunemba okuphansi kusho izinombolo ezengeziwe ezicutshungulwa iwashi ngalinye, okukhuphula ngokumangalisayo umphumela wokuqeqeshwa nokuchazwa kuyilapho kugcinwa ukunemba kwamukelekile.
I-Technical Insight
I-Tensor Core iphindaphinda omatikuletsheni ababili abancane futhi iqongelela umphumela ngesinyathelo esisodwa esihlanganisiwe, isebenzisa iqiniso lokuthi amanani afanayo okokufaka aphinde asetshenziswe ezintweni eziningi zokukhiphayo. Ngokuvamile ifunda okokufaka ngokunemba okuncishisiwe (FP16, BF16, noma FP8) kodwa iqongelela isamba esisebenzayo ngokunemba okuphezulu (ngokuvamile i-FP32) ukuze ikhawulele iphutha lokusondeza. Imitapo yolwazi yesofthiwe efana ne-cuBLAS ne-cuDNN, nezinhlaka ezifana ne-PyTorch, amathayela omatikuletsheni abakhulu kulawa mabhulokhi amancane ngokuzenzakalelayo ukuze amamodeli athole ukusheshisa ngaphandle kokufaka amakhodi okwenziwa ngesandla.
I-Mastering Tensor Cores
I-Tensor Cores amayunithi ehadiwe akhethekile ngaphakathi kwe-NVIDIA GPU yesimanje enza imisebenzi ye-matrix yokuphindaphinda nokuqongelela ngokushesha okukhulu. Kuyisizathu esiyinhloko sokuthi i-GPU eyodwa ingaqeqesha futhi isebenzise ama-oda amakhulu wenethiwekhi ye-neural yobukhulu ngokushesha kunokuba ikhompuyutha yenjongo evamile ingavumela. I-Tensor Cores iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini. Ukuze wakhe ukuqonda okujulile, phatha i-Tensor Cores njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho isistimu engakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Tensor Cores athuthukisa ukwakheka, idatha, nokukhetha kwengqalasizinda ngokumelene nokuthembeka nezindleko. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ngesikhathi esifanayo, Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Ukuqeqesha amamodeli amakhulu olimi afana neziguquli zesitayela se-GPT, lapho izigidigidi zokuphindaphinda kwe-matrix isinyathelo ngasinye zisebenza ku-Tensor Cores ku-BF16 noma ku-FP8.
Isebenzisa ukulinganisa kwesikhathi sangempela kuma-chatbots namajeneretha ezithombe, kusetshenziswa ukulinganisa kwe-INT8 noma i-FP8 ukuze kunikezwe abasebenzisi abengeziwe nge-GPU ngayinye.
Ukusheshisa i-NVIDIA DLSS kumageyimu evidiyo, lapho inethiwekhi ye-neural inyusa izinga lozimele abanokulungiswa okuphansi isebenzisa i-Tensor Cores ifreyimu ngayinye.
Ukusheshisa ikhompuyutha yesayensi efana ne-protein-folding (AlphaFold) namamodeli wesimo sezulu ahlelwe kabusha njengemithwalo ye-matrix-heavy neural workload.
Amaphethini Okusebenzisa
Ama-Tensor Cores ekusebenzeni
Ukuqeqesha amamodeli amakhulu olimi afana neziguquli zesitayela se-GPT, lapho izigidigidi zokuphindaphinda kwe-matrix isinyathelo ngasinye zisebenza ku-Tensor Cores ku-BF16 noma ku-FP8.
Ukuqeqesha amamodeli amakhulu olimi afana neziguquli zesitayela se-GPT, lapho izigidigidi zokuphindaphinda kwe-matrix isinyathelo ngasinye zisebenza ku-Tensor Cores ku-BF16 noma amaQembu e-FP8 ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Ama-Tensor Cores ekusebenzeni
Isebenzisa ukulinganisa kwesikhathi sangempela kuma-chatbots namajeneretha ezithombe, kusetshenziswa ukulinganisa kwe-INT8 noma i-FP8 ukuze kunikezwe abasebenzisi abengeziwe nge-GPU ngayinye.
Ukusebenzisa isiphetho sesikhathi sangempela sama-chatbots namajeneretha ezithombe, kusetshenziswa ukulinganisa kwe-INT8 noma i-FP8 ukuze kusetshenziswe abasebenzisi abengeziwe ngamaQembu e-GPU ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, agcine indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Ama-Tensor Cores ekusebenzeni
Ukusheshisa i-NVIDIA DLSS kumageyimu evidiyo, lapho inethiwekhi ye-neural inyusa izinga lozimele abanokulungiswa okuphansi isebenzisa i-Tensor Cores ifreyimu ngayinye.
Ukusheshisa i-NVIDIA DLSS kumageyimu wevidiyo, lapho inethiwekhi ye-neural iphakamisa amafreyimu anokulungiswa okuphansi isebenzisa i-Tensor Cores Uhlaka ngalunye Amaqembu ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, agcine indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Ama-Tensor Cores ekusebenzeni
Ukusheshisa ikhompuyutha yesayensi efana ne-protein-folding (AlphaFold) namamodeli wesimo sezulu ahlelwe kabusha njengemithwalo ye-matrix-heavy neural workload.
Ukusheshisa ukusebenzisa ikhompuyutha yesayensi njengokugoqa amaprotheni (AlphaFold) kanye namamodeli wesimo sezulu aphinde aguqulwa njengemithwalo ye-matrix-heavy neural work Amaqembu ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu.
Izindleko zengqalasizinda nezokulungisa zivame ukubukelwa phansi.
Izikhala zokuphepha nokubonakala zingakhula njengoba izinhlelo ziba nzima kakhulu.
Ukuqalisa Umhlahlandlela
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa.
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.