Venue: 2022 IEEE 15th International Conference on Cloud Computing (CLOUD), work-in-progress, Acceptance rate 25.5% (49/192), NRF BK IF: 1, Barcelona, Spain, 2022, pp. 112-114, doi: 10.1109/CLOUD55607.2022.00030. (2022)
Deep learning models have a wide spectrum of GPU execution time and memory size. When running distributed training jobs, however, their GPU execution time and memory size have not been taken into account, which leads to the high variance of job completion time (JCT). Moreover, the jobs often run into the GPU out-of-memory (OoM) problem so that the unlucky job has to restart all over. To address the problems, we propose Xonar to profile the deep learning jobs and order them in the queue. The experiments show that Xonar with TensorFlow v1.6 reduces the tail JCT by 44% with the OoM problem eliminated.