Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning
Shubham Chaudhary, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Srinidhi Viswanatha
Abstract
We present Gandivafair, a distributed, fair share scheduler that balances conflicting goals of efficiency and fairness in GPU clusters for deep learning training (DLT). Gandivafair provides performance isolation between users, enabling multiple users to share a single cluster, thus, maximizing cluster efficiency. Gandivafair is the first scheduler that allocates cluster-wide GPU time fairly among active users.
Topics & Concepts
Computer scienceGPU clusterCluster (spacecraft)Distributed computingScheduling (production processes)Parallel computingComputer networkCUDAEconomicsOperations managementStochastic Gradient Optimization TechniquesCloud Computing and Resource ManagementAdvanced Neural Network Applications