Presentation
EffiPipe: Towards Energy-Efficient Large-scale Model Training on Commodity GPUs
DescriptionWith the continuous evolution of large models, large model
training has become increasingly critical. However, large-
scale model training typically requires a substantial energy
consumption, which adds to the cost of training these mod-
els. We present EffiPipe, an energy-efficient GPU scheduling
system for large-scale model training tasks. EffiPipe con-
ducts fine-grained scheduling of operators. Incorporating
dynamic frequency adjustment for both computing and mem-
ory, and taking into account distributed model training sce-
narios.Compared to existing works, we can reduce power
consumption by 20-30% while ensuring performance is main-
tained
training has become increasingly critical. However, large-
scale model training typically requires a substantial energy
consumption, which adds to the cost of training these mod-
els. We present EffiPipe, an energy-efficient GPU scheduling
system for large-scale model training tasks. EffiPipe con-
ducts fine-grained scheduling of operators. Incorporating
dynamic frequency adjustment for both computing and mem-
ory, and taking into account distributed model training sce-
narios.Compared to existing works, we can reduce power
consumption by 20-30% while ensuring performance is main-
tained
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security