UMHLAHLANDLELA Wobuchwepheshe

Collective Communication kanye NCCL

Ukuxhumana okuhlangene yindlela iqembu lama-GPUs elishintshana ngayo futhi lihlanganise idatha, futhi i-NCCL ilabhulali ye-NVIDIA eyenza lokho kushintshana kusheshe kakhulu.

2 amaminithi ukufundaIgcine ukubuyekezwa

Uhlolojikelele

Operations like all-reduce are the heartbeat of distributed training, synchronizing gradients across every GPU each step.

I-Deep Dive

Ukuqeqesha imodeli enkulu kusho ukuthi i-GPU ngayinye ibala ama-gradient esiqeshini sayo sedatha, bese wonke ama-GPU kufanele avumelane ngomphumela ohlanganisiwe ngaphambi kwesinyathelo esilandelayo. Lokho kuhlanganisa kwenziwa ngemisebenzi eyiqoqo: nciphisa konke amanani ezibalo kuwo wonke ama-GPU futhi kunikeza wonke umuntu umphumela; iqoqo ngalinye liqoqa ucezu ngalunye lwe-GPU lube ikhophi egcwele kuwo wonke; ukusakaza kuthumela idatha ye-GPU eyodwa kokusele; ukunciphisa-scatter kuhlanganisa bese kuyahlukana. I-NCCL (I-NVIDIA Collective Communications Library) isebenzisa kahle lokhu kuwo wonke ama-GPU kuseva nakuwo wonke amaseva, isebenzisa ama-algorithms aqaphela i-topology njengeringi nesihlahla nciphisa konke. Isebenzisa i-NVLink ngaphakathi kwe-node kanye ne-InfiniBand noma i-RoCE phakathi kwama-node, futhi iwumgogodla wokuxhumana ngaphansi kwe-PyTorch DDP, i-FSDP, i-DeepSpeed, ne-Megatron.

I-Technical Insight

Ukunciphisa konke kuyi-algorithm yakudala: Ama-GPU enza indandatho enengqondo, futhi idatha ihlukaniswa yaba izingcezu ezijikelezayo ukuze isinyathelo ngasinye sidlulele ekukhulumisaneni, okwenza isamba somkhawulokudonsa sokudlulisa sibe-lilungile futhi sicishe sizimele ekubalweni kwe-GPU. Kuma-node amaningi, ama-algorithms asekelwe esihlahleni anciphisa ukubambezeleka ngokuhlanganisa imiphumela ngokohlelo. I-NCCL ihlonza ngokuzenzakalelayo i-topology, ikhetha i-algorithm ehamba phambili, futhi ingakhipha izibalo zokunciphisa kunethiwekhi nge-NVIDIA SHARP, ihlukanise uhhafu idatha okufanele yeqe izixhumanisi.

I-Strategic Impact

Izindleko kanye nesabelomali

Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.

Izinqumo ezicacile

Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.

Ukulawulwa kwekhwalithi

Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.

Ikusasa Lokuxhumanisa Ngokuhlanganyela kanye ne-NCCL

Njengoba amaqoqo afinyelela kumakhulu ezinkulungwane zama-GPU, ukuxhumana kuya ngokuya kubusa isikhathi sokuqeqeshwa, ngakho-ke imitapo yolwazi iwumngcele oshisayo. Lindela ukujula kwekhompuyutha ye-in-network (ukushintsha ukwenza ukunciphisa), ukugqagqana okungcono kwekhompuyutha nokuxhumana ukuze ufihle ukubambezeleka, namaqoqo anonemba kancane ashwabanisa amabhayithi anyakazayo. Ukuncintisana kuyakhula futhi, ngemizamo yomthengisi onqamulayo kanye ne-Ethernet-based RDMA yokuphusha ezinye izindlela, kuyilapho i-NCCL ilokhu iqinisa ukuhlanganiswa ne-NVLink, NVSwitch, nezindwangu zamehlo ezivelayo.

Ukuqaliswa Komhlaba Wangempela

Ukuvumelanisa ama-gradient zonke izinyathelo zokuqeqesha kuwo wonke ama-GPU kusetshenziswa ukuncishiswa konke ku-PyTorch DistributedDataParallel

Ukwabelana ngezimo ze-optimizer kanye nokuqoqa amapharamitha ngokufunwa ngakho konke ukuqoqa nokunciphisa ukuhlakazeka ku-FSDP noma i-DeepSpeed ​​ZeRO

Ukusakaza izisindo zemodeli yokuqala ukusuka ku-GPU eyodwa kuya kuzo zonke ezinye ekuqaleni komgijimo wokuqeqesha

Ukusebenzisa indandatho yokunciphisa konke nge-NVLink ne-InfiniBand ukuze ugcine umkhawulokudonsa uphezulu kuwo wonke amaqoqo e-GPU enamanodi amaningi

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

1

Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa.

2

Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.

3

Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.

4

Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.

Qhubeka Uhlole

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Umhlahlandlela olandelayo

Isici Esiku-inthanethi Nesingaxhunyiwe Ku-inthanethi Sokusebenzela I-skew

Imibuzo evame ukubuzwa

What is Collective Communication and NCCL?

Ukuxhumana okuhlangene yindlela iqembu lama-GPUs elishintshana ngayo futhi lihlanganise idatha, futhi i-NCCL ilabhulali ye-NVIDIA eyenza lokho kushintshana kusheshe kakhulu. Imisebenzi efana nokunciphisa konke ukushaya kwenhliziyo kokuqeqeshwa okusabalalisiwe, ukuvumelanisa ama-gradient kuyo yonke i-GPU ngayinye isinyathelo.

Yini efezwa umsebenzi wokunciphisa konke ekuqeqesheni okusabalalisiwe?

Nciphisa konke izibalo (noma ukuhlanganisa) inani kuyo yonke i-GPU futhi kusabalalise umphumela ofanayo kuwo wonke, okuyindlela ama-gradient avunyelaniswa ngayo.

Imelelani isifinyezo esithi NCCL?

I-NCCL I-NVIDIA Collective Communications Library, esebenzisa ama-GPU amaningi asheshayo kanye nemisebenzi ehlangene yamanodi amaningi.

Kungani ukuncishiswa kwendandatho kubhekwa njenge-bandwidth-optimal?

Ngokuhlanganisa idatha nokudlulisa izingcezu eringini enengqondo, sonke isixhumanisi sisetshenziswa ngesikhathi esisodwa futhi ukudluliswa okuphelele kucishe kuzimele enanini lama-GPU.

Yikuphi ukusebenza okuhlangene okuqoqa ingxenye yedatha ye-GPU ngayinye ibe ikhophi egcwele ephethwe yiwo wonke ama-GPU?

I-All-Gather iqoqa izingcezu ezihlukile kuzo zonke i-GPU futhi ihlanganise isethi ephelele kuzo zonke, ezisetshenziswa kakhulu ekuqeqeshweni okuhlukene njenge-FSDP.

Yenzani i-NVIDIA SHARP ekusebenzeni okuhlangene?

I-SHARP yenza i-in-network computing, yenza ingxenye yokunciphisa ngaphakathi kweswishi ukuze idatha encane inqamule izixhumanisi, isheshise amaqoqo.