Tensor Parallelism yeMakuru Models Mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinonzi tensor parallelism kupatsanurwa mukati meGPUs?
   • Huremu hwematrices mukati mechikamu chimwe chete
   • Matunhu ese akaiswa pamaGPU akasiyana
   • Zvidzidzo zvakasiyana-siyana zvekudzidzisa
   • The optimizer kudzidza mwero
2. MuMegatron-style MLP kupatsanurwa, iwo maviri uremu matrices akagovaniswa sei kuderedza kutaurirana?
   • Ose ari maviri column-akachenjera
   • Yokutanga pambiru-yakachenjera, yechipiri-huchenjeri
   • Ose-mumutsara
   • Havana kupatsanurwa zvachose
3. Sei tensor parallelism ichiwanzo chengetwa mukati menodhi imwechete?
   • Node haigone kumhanya maGPU
   • MaGPU munzvimbo dzakasiyana anoshandisa masvomhu akasiyana
   • Inoda kazhinji yakakwira-bandwidth kutaurirana zvakanyanya kushandirwa neNVLink
   • Chinhu chinodiwa nemutemo
4. Maitiro ekutarisa anowanzo kufananidzwa pasi petensor parallelism?
   • Nekupa yega GPU iyo yakazara yekutarisisa computation zvakare
   • Nokudarika kutarisisa zvachose
   • Nokushandura pfungwa kune convolution
   • Nekuparadzanisa misoro yekutarisisa, yega yega GPU inobata subset
5. Ndeipi mashandiro emubatanidzwa anowanzo kusanganisa mhedzisiro mutensor parallelism?
   • Kuderedza-zvose
   • A file copy
   • A database join
   • A DNS kutarisa
6. Ko tensor parallelism inowanzoenderana sei mu'3D parallelism'?
   • Inotsiva mamwe marudzi ese ekuenzanisa
   • Iyo inosanganiswa nepipeline uye data parallelism
   • Inongoshanda pasina kumwe kuenderana
   • Inoshandiswa chete kumifananidzo yemifananidzo
7. After the row-wise partition of an MLP’s second matrix, why must GPUs combine partial outputs?
   • The GPUs each hold a different optimizer
   • The result is a vocabulary probability that must be sorted
   • Each GPU computed a partial contribution to the same output, so the pieces must be summed
   • The model needs to average separate datasets
8. Why can interconnect latency limit tensor-parallel scaling across distant nodes?
   • Tensor parallelism never communicates between GPUs
   • The attention mask must be copied after every token
   • Every GPU trains a separate model
   • Collectives are required repeatedly within layers, so communication latency accumulates