1 research outputs found
Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters
The freedom of fast iterations of distributed deep learning tasks is crucial
for smaller companies to gain competitive advantages and market shares from big
tech giants. HorovodRunner brings this process to relatively accessible spark
clusters. There have been, however, no benchmark tests on HorovodRunner per se,
nor specifically graph convolutional network (GCN, hereafter), and very limited
scalability benchmark tests on Horovod, the predecessor requiring custom built
GPU clusters. For the first time, we show that Databricks' HorovodRunner
achieves significant lift in scaling efficiency for the convolutional neural
network (CNN, hereafter) based tasks on both GPU and CPU clusters, but not the
original GCN task. We also implemented the Rectified Adam optimizer for the
first time in HorovodRunner.Comment: AAAI 2020 W8 Deep Learning on Graphs: Methodologies and Applications
Accepted Poster Number 2