October 02, 2018
Sung-Han Lin, NetApp; Marco Paolieri, University of Southern California; Cheng-Fu Chou, National Taiwan University; Leana Golubchik, University of Southern California
the 26th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2018)
September 25 - 28, 2018 Milwaukee, Wisconsin, US.
The success of Deep Neural Networks (DNNs) has created significant interest in the development of software tools, hardware architectures, and cloud systems to meet the huge computational demand of their training jobs. A common approach to speeding up an individual job is to distribute training data and computation among multiple nodes, periodically exchanging intermediate results. In this paper, we address two important problems for the application of this strategy to large-scale clusters and multiple, heterogeneous jobs. First, we propose and validate a queueing model to estimate the throughput of a training job as a function of the number of nodes assigned to the job; this model targets asynchronous Stochastic Gradient Descent (SGD), a popular strategy for distributed training, and requires only data from quick, two-node profiling in addition to job characteristics (number of requested training epochs, mini-batch size, size of DNN parameters, assigned bandwidth). Throughput estimations are then used to explore several classes of scheduling heuristics to reduce response time in a scenario where heterogeneous jobs are continuously submitted to a large-scale cluster. These scheduling algorithms dynamically select which jobs to run and how many nodes to assign to each job, based on different trade-offs between service time reduction and efficiency (e.g., speedup per additional node). Heuristics are evaluated through extensive simulations of realistic DNN workloads, also investigating the effects of early termination, a common scenario for DNN training jobs.
A copy of the paper can be found at: http://www.mscs.mu.edu/~mascots/Papers/34.pdf.