GPU Scheduler
Sistem Analizi
Normal Davranış
When an ML training job or inference pod is scheduled, the GPU scheduler inspects available accelerator VRAM and interconnect topology, binds specific GPU devices to the container cgroup, and enforces memory quotas.
Çöküş Davranışı
If the scheduler assigns workloads without accounting for memory fragmentation or thermal throttling, jobs fail with CUDA out-of-memory errors, low-priority jobs starve production inference, or unallocated GPUs sit idle due to topology lockouts.
İş Sonuçları
Unknown
Bilinen İsimler
Teknik Terminoloji
Hata Göstergeleri
Sistem Mimarisi
FAQ
Normalde nasıl davranır?
When an ML training job or inference pod is scheduled, the GPU scheduler inspects available accelerator VRAM and interconnect topology, binds specific GPU devices to the container cgroup, and enforces memory quotas.
Nasıl çöker?
If the scheduler assigns workloads without accounting for memory fragmentation or thermal throttling, jobs fail with CUDA out-of-memory errors, low-priority jobs starve production inference, or unallocated GPUs sit idle due to topology lockouts.
İş sonuçları nelerdir?
Unknown
What is the difference between GPU time-slicing and Multi-Instance GPU (MIG)?
Time-slicing allows multiple containers to share a GPU by rapidly interleaving execution, but provides no memory isolation—if one container over-allocates VRAM, all containers crash. Multi-Instance GPU (MIG) physically partitions the GPU into up to 7 hardware-isolated instances with dedicated memory, cache, and compute slices, guaranteeing complete fault isolation.
Sistemi keşfet
AI özeti
GPU Scheduler is a undefined system in TinyCTO.tv. When an ML training job or inference pod is scheduled, the GPU scheduler inspects available accelerator VRAM and interconnect topology, binds specific GPU devices to the container cgroup, and enforces memory quotas.