DLoBD: A Comprehensive Study of Deep Learning over Big Data Stacks on HPC Clusters

Abstract

<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> eep <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> ver <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> ig <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ata (DLoBD) is an emerging paradigm to mine value from the massive amount of gathered data. Many Deep Learning frameworks, like Caffe, TensorFlow, etc., start running over Big Data stacks, such as Apache Hadoop and Spark. Even though a lot of activities are happening in the field, there is a lack of comprehensive studies on analyzing the impact of RDMA-capable networks and CPUs/GPUs on DLoBD stacks. To fill this gap, we propose a systematical characterization methodology and conduct extensive performance evaluations on four representative DLoBD stacks (i.e., CaffeOnSpark, TensorFlowOnSpark, MMLSpark/CNTKOnSpark, and BigDL) to expose the interesting trends regarding performance, scalability, accuracy, and resource utilization. Our observations show that RDMA-based design for DLoBD stacks can achieve up to 2.7x speedup compared to the IPoIB-based scheme. The RDMA scheme also scales better and utilizes resources more efficiently than IPoIB. For most cases, GPU-based schemes can outperform CPU-based designs, but we see that for LeNet on MNIST, CPU + MKL can achieve better performance than GPU and GPU + cuDNN on 16 nodes. Through our evaluation and an in-depth analysis on TensorFlowOnSpark, we find that there are large rooms to improve the designs of current-generation DLoBD stacks.