The performance of Deep-Learning (DL) computing frameworks rely on the
performance of data ingestion and checkpointing. In fact, during the training,
a considerable high number of relatively small files are first loaded and
pre-processed on CPUs and then moved to accelerator for computation. In
addition, checkpointing and restart operations are carried out to allow DL
computing frameworks to restart quickly from a checkpoint. Because of this, I/O
affects the performance of DL applications. In this work, we characterize the
I/O performance and scaling of TensorFlow, an open-source programming framework
developed by Google and specifically designed for solving DL problems. To
measure TensorFlow I/O performance, we first design a micro-benchmark to
measure TensorFlow reads, and then use a TensorFlow mini-application based on
AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow.
To improve the checkpointing performance, we design and implement a burst
buffer. We find that increasing the number of threads increases TensorFlow
bandwidth by a maximum of 2.3x and 7.8x on our benchmark environments. The use
of the tensorFlow prefetcher results in a complete overlap of computation on
accelerator and input pipeline on CPU eliminating the effective cost of I/O on
the overall performance. The use of a burst buffer to checkpoint to a fast
small capacity storage and copy asynchronously the checkpoints to a slower
large capacity storage resulted in a performance improvement of 2.6x with
respect to checkpointing directly to slower storage on our benchmark
environment.Comment: Accepted for publication at pdsw-DISCS 201