Data Parallelism
Dhata parallelism inodzidzisa modhi imwe nekukurumidza nekuidzokorora kune akawanda maGPU, neGPU yega yega inogadzira chikamu chakasiyana che data batch.
Pfupiso
It is the workhorse technique that lets teams scale to dozens or thousands of accelerators.
Kudzika Kwakadzika
Mune data parallelism, yega GPU inobata kopi yakafanana yehuremu hwemuenzaniso asi inogadzirisa yakasarudzika mini-batch yemuenzaniso wekudzidziswa. Chishandiso chega chega chinomisikidza chipfuura chekumberi nekumashure chakazvimirira, chichigadzira seti yaro yemagradients. Uremu husati hwavandudzwa, ma gradients anoverengerwa pamaGPU ese achishandisa zvese-kuderedza kutaurirana mashandiro, saka replica yega yega inogara mukuwirirana uye inoita sekunge yakadzidziswa pane imwe hombe yakasanganiswa batch. Izvi zvinonyatso wedzera kubuda: 8 GPUs inogona kutsenga kuburikidza ne8x iyo data padanho. Iyo inobata ndeyekuti yega yega GPU inofanirwa kukwana iyo yese modhi, ma gradients, uye optimizer mamiriro mundangariro, saka yakajeka data parallelism haibatsire kana modhi yakakurisa kune chimwe chinhu.
Technical Insight
Kushanda kwakakosha ndekwese-kudzikisa, iyo inoverengera gradients pamidziyo yese uye kugovera mhedzisiro. Ring-yese-inoderedza, inoshandiswa nemaraibhurari seNCCL neHorovod, inopfuudza gradient chunks kutenderedza mhete inonzwisisika saka kutaurirana kwakazara kwakazvimirira paGPU kuverenga. PyTorch's DistributedDataParallel inoputira iyi kutaurirana neiyo yekumashure pass, ichidzima gradient sync kune ekutanga maseru apo gare gare maseru ichiri komputa, ichivanza yakawanda yetiweki latency.
Strategic Impact
Mutengo uye bhajeti
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
Sarudzo dzakajeka
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
Kudzora kwemhando yepamusoro
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Ramangwana reData Parallelism
Yakachena data parallelism iri kuwedzera kusanganiswa ne sharding uye modhi parallelism kuita yakasanganiswa 'nD parallelism' nzira dzematrillion-parameter modhi. Tarisira yakangwara gradient compression, asynchronous uye kupindirana kutaurirana, uye topology-inoziva zvese-kuderedza iyo inoshandisa nekukurumidza NVLink mukati me node uye inononoka InfiniBand mhiri node. Sezvo masumbu achikura, kudzikisira kutaurirana-kune-komputa reshiyo inoramba iri pakati peinjiniya dambudziko rekuchengeta zviuru zveGPU zvakabatikana.
Real-World Implementation
Kudzidzisa ResNet mufananidzo classifier mhiri 8 GPUs mune imwe sevha uchishandisa PyTorch DistributedDataParallel, yega yega GPU inobata makumi matatu nemaviri e256-mufananidzo batch.
Kuyera BERT pretraining mhiri kwemazana eGPUs neHorovod, uchishandisa mhete zvese-kuderedza kuwiriranisa gradients nhanho imwe neimwe.
Kunyatsogadzirisa modhi yekurudziro pane akawanda-node cluster apo imwe neimwe node inogadzira akasiyana-siyana-yekudyidzana shards.
Kushandisa TensorFlow's MirroredStrategy kuparadzira kudzidziswa kwemuenzaniso wechiratidzo pane akawanda maGPU pane imwe nzvimbo yekushandira ine mashoma kodhi shanduko.
Njodzi & Guardrails
Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Implementation Roadmap
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
Benchmark pasi pechokwadi mutoro uye data mamiriro.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
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What is Data Parallelism?
Dhata parallelism inodzidzisa modhi imwe nekukurumidza nekuidzokorora kune akawanda maGPU, neGPU yega yega inogadzira chikamu chakasiyana che data batch. Ndiyo nzira yekushanda inoita kuti zvikwata zvikwire kusvika kune gumi nemaviri kana zviuru zveanomhanyisa.
Mune yakajairwa data parallelism, GPU yega yega inobata chii?
Imwe neimwe GPU inochengeta yakazara yakazara modhi uye inogadzirisa chikamu chakasiyana che data batch, izvo ndizvo zvinoita kuti ive 'data' parallelism kwete modhi parallelism.
Ndeipi mashandiro ekutaurirana anochengeta modhi replicas mukuwiriranisa nhanho imwe neimwe?
Mushure mekupfuura kwega kwega kumashure, ma gradients anosanganisirwa pamidziyo kuburikidza ne-ese-kuderedza (kazhinji inopfupikiswa uye yakaenzana) saka replica yega yega inoshandisa zvakafanana kuvandudza.
Chii chinodzikisira chikuru cheplain data parallelism?
Nekuti yega yega GPU inobata kopi izere yezvese, data parallelism haina chayanobatsira kana modhi yakangonyanya kukura kuti ikwane pane chimwe chinhu.
Nei mhete yese-inodzikisira inoyevedza kune yakakura maGPU kuverenga?
Ring zvese-zvinoderedza zvinopfuura gradient chunks zvakatenderedza mhete ine musoro, saka iyo yakazara bandwidth yega yega GPU inotumira inogara isingaite zvisinei nekuti maGPU mangani anotora chikamu.
PyTorch DistributedDataParallel inovanza sei kutaurirana latency?
DDP inotanga kuwiriranisa ma gradients ematanho ekutanga apo gare gare maseru achiri kuverengerwa, achipfuura netiweki kutaurirana nekomputa.