Delivering Scalable Deep Learning to Research with Bridges-AI

Artificial intelligence (AI), particularly deep learning, is enabling tremendous advances and is itself of great research interest. To address these research requirements, the Pittsburgh Supercomputing Center (PSC) expanded its Bridges supercomputer with Bridges-AI, providing the world’s most powerful AI servers to the U.S. national research community and their international collaborators. We describe the motivation and architecture of Bridges-AI and its integration with Bridges, which adds to Bridges’ capabilities for scalable, converged high-performance computing (HPC), AI, and Big Data. We then describe the software environment of Bridges-AI, particularly the introduction of containers for deep learning frameworks, machine learning, and graph analytics, and PSC’s approach to container deployment. We close with a discussion of the range of research challenges that Bridges-AI is enabling breakthroughs, highlighting development of AI-driven methods to identify immune responses with automated tumor detection in breast cancer.

[1]  Philip D. Blood,et al.  Bridges: Converging HPC, AI, and Big Data for Enabling Discovery , 2019, Contemporary High Performance Computing.

[2]  Gregory M. Kurtzer,et al.  Singularity 2.1.2 - Linux application and environment containers for science , 2016 .

[3]  Ashish Sharma,et al.  Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer , 2019, The American journal of pathology.

[4]  Rajarsi R. Gupta,et al.  Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. , 2018, Cell reports.

[5]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[6]  Weihong Deng,et al.  Very deep convolutional neural network based image classification using small training sample size , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Nicholas A. Nystrom,et al.  Open Compass: Accelerating the Adoption of AI in Open Research , 2019, PEARC.

[9]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Richard Underwood.  Building Bridges: The System Administration Tools and Techniques Used to Deploy Bridges , 2017, PEARC.

[12]  Ralph Roskies,et al.  Bridges: a uniquely flexible HPC resource for new communities and data analytics , 2015, XSEDE.