@conference {,
	title = {DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models},
	booktitle = {20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)},
	year = {2020},
	month = {2020-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Melbourne, VIC, Australia},
	abstract = {In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.},
	doi = {https://doi.org/10.1109/CCGrid49817.2020.00-76},
	author = {Bogdan Nicolae and Jiali Li and Justin M. Wozniak and George Bosilca and Matthieu Dorier and Franck Cappello}
}
@techreport {1408,
	title = {A Collection of White Papers from the BDEC2 Workshop in San Diego, CA},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-13},
	year = {2019},
	month = {2019-10},
	publisher = {University of Tennessee},
	author = {Ilkay Altintas and Kyle Marcus and Volkan Vural and Shweta Purawat and Daniel Crawl and Gabriel Antoniu and Alexandru Costan and Ovidiu Marcu and Prasanna Balaprakash and Rongqiang Cao and Yangang Wang and Franck Cappello and Robert Underwood and Sheng Di and Justin M. Wozniak and Jon C. Calhoun and Cong Xu and Antonio Lain and Paolo Faraboschi and Nic Dube and Dejan Milojicic and Balazs Gerofi and Maria Girone and Viktor Khristenko and Tony Hey and Erza Kissel and Yu Liu and Richard Loft and Pekka Manninen and Sebastian von Alfthan and Takemasa Miyoshi and Bruno Raffin and Olivier Richard and Denis Trystram and Maryam Rahnemoonfar and Robin Murphy and Joel Saltz and Kentaro Sano and Rupak Roy and Kento Sato and Jian Guo and Jen s Domke and Weikuan Yu and Takaki Hatsui and Yasumasa Joti and Alex Szalay and William M. Tang and Michael R. Wyatt II and Michela Taufer and Todd Gamblin and Stephen Herbein and Adam Moody and Dong H. Ahn and Rich Wolski and Chandra Krintz and Fatih Bakir and Wei-tsung Lin and Gareth George}
}
@conference {,
	title = {Understanding Scalability and Fine-Grain Parallelism of Synchronous Data Parallel Training},
	booktitle = {2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)},
	year = {2019},
	month = {2019-11},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Denver, CO},
	abstract = {In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. With increasing complexity of learning models and amounts of training data, data-parallel approaches based on frequent all-reduce synchronization steps are increasingly popular. Despite the fact that high-performance computing (HPC) technologies have been designed to address such patterns efficiently, the behavior of data-parallel approaches on HPC platforms is not well understood. To address this issue, in this paper we study the behavior of Horovod, a popular data-parallel approach that relies on MPI, on Theta, a pre-Exascale machine at Argonne National Laboratory. Using two representative applications, we explore two aspects: (1) how performance and scalability is affected by important parameters such as number of nodes, number of workers, threads per node, batch size; (2) how computational phases are interleaved withall-reduce communication phases at fine granularity and what consequences this interleaving has in terms of potential bottlenecks. Our findings show that pipelining of back-propagation, gradient reduction and weight updates mitigate the effects of stragglers during all-reduce only partially. Furthermore, there can be significant delays between weights update, which can be leveraged to mask the overhead of additional background operations that are coupled with the training.},
	doi = {https://doi.org/10.1109/MLHPC49564.2019.00006},
	author = {Jiali Li and Bogdan Nicolae and Justin M. Wozniak and George Bosilca}
}
