Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

@article{Huerta2020ConvergenceOA,
  title={Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure},
  author={Eliu A. Huerta and Asad Khan and Edward Davis and Colleen Bushell and William Gropp and Daniel S. Katz and V. Kindratenko and Seid Koric and William T. C. Kramer and Brendan McGinty and Kenton McHenry and Aaron Saxton},
  journal={Journal of Big Data},
  year={2020},
  volume={7},
  pages={1-12}
}
Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social… 

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References

SHOWING 1-10 OF 71 REFERENCES

Big data and extreme-scale computing

TLDR
It is argued that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing.

Enabling real-time multi-messenger astrophysics discoveries with deep learning

TLDR
The key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts are reviewed, and a number of recommendations to maximize their potential for scientific discovery are made.

DLHub: Model and Data Serving for Science

TLDR
This work presents the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications and shows that relative to other model serving systems, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed.

Training distributed deep recurrent neural networks with mixed precision on GPU clusters

TLDR
A distributed, data-parallel, synchronous training algorithm is implemented by integrating TensorFlow and CUDA-aware MPI to enable execution across multiple GPU nodes and making use of high-speed interconnects, facilitating neural network convergence at up to O(100) workers.

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

TLDR
The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.

The unreasonable effectiveness of deep learning in artificial intelligence

  • T. Sejnowski
  • Computer Science
    Proceedings of the National Academy of Sciences
  • 2020
TLDR
Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals.

HAL: Computer System for Scalable Deep Learning

TLDR
A custom management software stack is built to enable an efficient use of the system by a diverse community of users and provide guides and recipes for running deep learning workloads at scale utilizing all available GPUs.

Brown Dog: Leveraging everything towards autocuration

We present Brown Dog, two highly extensible services that aim to leverage any existing pieces of code, libraries, services, or standalone software (past or present) towards providing users with a

Machine learning accelerated topology optimization of nonlinear structures

TLDR
Two convolutional neural network models are developed to predict optimized designs for a given set of boundary conditions, loads, and volume constraints and are capable of accurately predicting the optimized designs without requiring an iterative scheme and with negligible computational time.

An effective algorithm for hyperparameter optimization of neural networks

TLDR
This paper addresses the problem of choosing appropriate parameters for the NN by formulating it as a box-constrained mathematical optimization problem, and applying a derivative-free optimization tool that automatically and effectively searches the parameter space.
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