Maswali ya Usimamizi wa Kumbukumbu ya GPU na Kugawanyika

Majibu na maelezo huonekana unapojibu maswali.

1. Mgawanyiko wa kumbukumbu ya GPU ni nini?
   • Wakati GPU inatengana kimwili
   • Wakati mfano una tabaka nyingi
   • Wakati GPU mbili hazikubaliani juu ya matokeo
   • Wakati kumbukumbu isiyolipishwa inagawanywa katika vipande vilivyotawanyika vidogo sana kutosheleza ombi kubwa linaloambatana
2. Kwa nini mifumo kama PyTorch hutumia kigawanya kumbukumbu ya kache?
   • Ili kufanya mfano kuwa sahihi zaidi
   • Ili kuzuia ugawaji wa polepole wa dereva / simu za bure kwa kutumia tena vizuizi vya kumbukumbu vilivyounganishwa
   • Ili kusimba uzani
   • Ili kupunguza idadi ya GPU zinazohitajika hadi moja
3. A CUDA allocation fails despite apparently unused reserved memory. What should a developer investigate?
   • Whether fragmentation is proven by the error alone
   • Whether all live tensors have already been freed
   • Whether inactive split blocks or other allocations explain the failure
   • Whether changing the learning rate releases GPU memory
4. Which PyTorch allocator option can be tested for changing-size workloads that leave unused memory slivers?
   • Expandable segments in the allocator configuration
   • Increasing every tensor dimension
   • Disabling the GPU cooling system
   • Increasing the optimizer learning rate
5. PagedAttention ya vLLM inasimamia nini kupunguza upotevu wa kumbukumbu wakati wa uelekezaji?
   • Data ya mafunzo ya mfano
   • Akiba ya ufunguo wa thamani (KV), kwa kutumia kurasa za ukubwa usiobadilika
   • Kasi ya saa ya GPU
   • Saizi ya fonti ya kumbukumbu
6. Ni mbinu gani inapunguza kumbukumbu ya kuwezesha ili kusaidia kuzuia makosa ya nje ya kumbukumbu?
   • Kuongeza tabaka zaidi
   • Kwa kutumia nyongeza ndefu ya kiwango cha kujifunza
   • Ukagua gredi (recomputation)
   • Inahifadhi vituo zaidi vya ukaguzi kwenye diski
7. A live CUDA tensor still has Python references. Will empty_cache release that tensor’s allocation?
   • No; it releases unused cached memory rather than live tensor storage
   • Yes; it deletes every referenced tensor
   • Yes; it moves the tensor automatically to NVMe
   • Only if the tensor contains zero values
8. PyTorch reports reserved memory greater than allocated memory. What distinction explains the gap?
   • Reserved memory measures only CPU RAM
   • Allocated memory includes every process on the GPU
   • The difference directly proves a model accuracy error
   • The allocator can retain cached blocks beyond memory currently used by tensors