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Akopọ Communication ati NCCL
Collective communication coordinates data across a group of participating devices.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
NCCL implements GPU communication operations such as all-reduce, all-gather and broadcast; their performance depends on message size, topology, hardware and configuration.
Jin Dive
Ikẹkọ awoṣe nla tumọ si pe GPU kọọkan ṣe iṣiro awọn gradients lori bibẹ pẹlẹbẹ data tirẹ, lẹhinna gbogbo awọn GPU gbọdọ gba lori abajade apapọ ṣaaju igbesẹ atẹle. Iṣọkan yẹn ni a ṣe pẹlu awọn iṣẹ apapọ: gbogbo-dindinku awọn iye akopọ kọja awọn GPU ati fun gbogbo eniyan ni abajade; gbogbo-apejọ n gba nkan GPU kọọkan sinu ẹda kikun lori gbogbo wọn; igbohunsafefe rán ọkan GPU ká data si awọn iyokù; din-tuka daapọ lẹhinna pin. NCCL (NVIDIA Collective Communications Library) ṣe imuse wọnyi daradara kọja awọn GPU ni olupin ati kọja awọn olupin, ni lilo awọn algoridimu ti o mọ topology bi oruka ati igi gbogbo-dinku. O nilokulo NVLink inu ipade kan ati InfiniBand tabi RoCE laarin awọn apa, ati pe o jẹ ẹhin ibaraẹnisọrọ labẹ PyTorch DDP, FSDP, DeepSpeed, ati Megatron.
Real-World imuse
Mimuuṣiṣẹpọ awọn gradients gbogbo igbesẹ ikẹkọ kọja gbogbo awọn GPU ni lilo gbogbo-dinku ni PyTorch DistributedDataParallel
Pinpin awọn ipinlẹ iṣapeye ati apejọ awọn aye lori ibeere pẹlu gbogbo-kojọpọ ati idinku-tuka ni FSDP tabi DeepSpeed ZeRO
Sisọ awọn iwọn awoṣe ibẹrẹ lati GPU kan si gbogbo awọn miiran ni ibẹrẹ ti ṣiṣe ikẹkọ
Lilo oruka gbogbo-din lori NVLink ati InfiniBand lati jẹ ki bandiwidi ga ga kọja awọn iṣupọ GPU-opopona pupọ
Ojo iwaju ti Ibaraẹnisọrọ Ajọpọ ati NCCL
Larger models and clusters motivate work on computation/communication overlap, topology-aware routing and network offloads. These can improve some workloads while adding configuration and debugging needs. Compare algorithms with representative tensor sizes and record versions and topology. Correct participation and buffer handling remain essential even when a library automates much of the routing. A link-bandwidth claim alone cannot establish end-to-end training speed.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Iye owo ati isuna. Awọn ipinnu faaji ṣe awakọ iṣẹ ati idiyele iṣẹ fun awọn ọdun.
- Awọn ipinnu diẹ sii. Ẹkọ imọ-ẹrọ ṣe iranlọwọ fun awọn ẹgbẹ lati yan akopọ to tọ, kii ṣe ọkan tuntun nikan.
- Iṣakoso didara. Awọn yiyan imọ-ẹrọ to dara julọ dinku awọn iṣẹlẹ igbẹkẹle ni iṣelọpọ.
Ṣọra fun
- Ṣiṣepe ala-ilẹ kan le tọju awọn ailagbara eto ti o gbooro.
- Awọn ohun elo amayederun ati awọn idiyele itọju nigbagbogbo ni aibikita.
- Aabo ati awọn ela akiyesi le dagba bi awọn eto ṣe di eka sii.
Next awọn igbesẹ
- Ṣetumo lairi, didara, ati awọn ibi-afẹde idiyele ṣaaju imuse.
- Aṣepari labẹ ẹru ojulowo ati awọn ipo data.
- Abojuto ohun elo fun awọn aṣiṣe, fiseete, ati ipa olumulo.
- Mura ipadasẹhin pada ati awọn ipa ọna esi iṣẹlẹ ṣaaju iwọn.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the Collective Communication and NCCL quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Kini ibaraẹnisọrọ apapọ ati NCCL?
Collective communication coordinates data across a group of participating devices. NCCL implements GPU communication operations such as all-reduce, all-gather and broadcast; their performance depends on message size, topology, hardware and configuration.
Kini iṣẹ-ṣiṣe idinku gbogbo ṣe aṣeyọri ni ikẹkọ pinpin?
Gbogbo-din awọn akopọ (tabi bibẹẹkọ daapọ) iye kan kọja gbogbo GPU ati pinpin abajade kanna pada si gbogbo wọn, eyiti o jẹ bi awọn gradients ṣe muṣiṣẹpọ.
Kini adape NCCL duro fun?
NCCL jẹ ile-ikawe Ibaraẹnisọrọ Ajọpọ NVIDIA, eyiti o ṣe imuse pupọ-GPU pupọ ati awọn iṣẹ apapọ apapọ-opopona.
Why can chunked ring communication be useful for a large tensor?
A ring organizes chunk movement, but performance still depends on the network and workload.
Iṣe apapọ wo ni o ṣajọ nkan data GPU kọọkan sinu ẹda kikun ti o waye nipasẹ gbogbo awọn GPU?
Gbogbo-apejọ n gba awọn ege ọtọtọ lati gbogbo GPU ati pe o pejọ pipe lori gbogbo wọn, ti a lo ni agbara ni ikẹkọ sharded bii FSDP.
What does a supported network reduction offload such as SHARP do?
Offload capability is configuration-dependent and must be evaluated.
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