MIGER: Integrating Multi-Instance GPU and Multi-Process Service for Deep Learning Clusters
Bowen Zhang, Shuxin Li, Zhuozhao Li
Abstract
Modern NVIDIA GPUs, known for their powerful computational abilities, have been widely adopted by data centers. These GPUs often use space-sharing techniques, such as Multi-Process Service (MPS) and Multi-Instance GPU (MIG), to run multiple workloads on a GPU concurrently. However, our findings reveal that there are issues such as performance interference and inflexible resource size for these techniques when they are used individually.
Topics & Concepts
Computer scienceProcess (computing)Deep learningService (business)GPU clusterArtificial intelligenceParallel computingMachine learningCUDAOperating systemEconomyEconomicsAdvanced Neural Network ApplicationsMedical Image Segmentation TechniquesParallel Computing and Optimization Techniques