Characterizing Deep-Learning I/O Workloads in TensorFlow

Abstract

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 2018

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1810.03035

• http://arxiv.org/pdf/1810.03035

• http://xplorestaging.ieee.org/ielx7/8630311/8638414/08638422.pdf?arnumber=8638422,

http://dx.doi.org/10.1109/pdsw-discs.2018.00011

• https://arxiv.org/pdf/1810.03035.pdf,

https://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-03035,

https://arxiv.org/abs/1810.03035,

http://www.diva-portal.org/smash/record.jsf?pid=diva2:1302569,

http://export.arxiv.org/pdf/1810.03035,

https://www.arxiv-vanity.com/papers/1810.03035,

https://fr.arxiv.org/abs/1810.03035,

http://export.arxiv.org/abs/1810.03035,

https://uk.arxiv.org/abs/1810.03035,

https://fr.arxiv.org/pdf/1810.03035,

https://academic.microsoft.com/#/detail/2951947775