# AI Case Study

## Cray's CosmoFlow can now predict cosmological parameters with unprecedented accuracy with a deep learning 3D convolutional neural network

Cray, the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory (NERSC), and Intel have developed CosmoFlow, a deep learning 3D convolutional neural network (CNN). The system is aimed at predicting cosmological parameters with great accuracy. Such parameters include density, matter density fluctuations and power law index of the density perturbation spectrum after inflation. The research team achieved the same level of accuracy as existing experiments in estimating the values of Ωm and σ8, and five times less error than previous measurements using deep learning in estimating the value of Ns.

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Project Overview

"Today, Cray, NERSC (the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory) and Intel announced the results of a three-way collaboration around CosmoFlow, a deep learning 3D convolutional neural network (CNN) that can predict cosmological parameters with unprecedented accuracy using the Intel-powered Cray® XC™ series “Cori” supercomputer at NERSC.

CosmoFlow is an evolutionary step, refining earlier work by a team at Carnegie Mellon University who proved that it was “possible to estimate … cosmological parameters directly from the distribution of matter” using 3D convolutional neural networks. CosmoFlow is built as a 3D convolutional neural network using large 3D datasets (generated by N-body simulations) — a combination usually avoided in typical projects using 3D data — where 3D data is converted to 2D because of the compute and I/O demands of 3D.

What makes CosmoFlow unique is the unprecedented level of accuracy achieved when compute resources at the scale of the NERSC Cori system are brought to bear. The three parameters — the density parameter describing the proportion of matter in the universe (Ωm), matter density fluctuations on scales of 8 (σ8), and the power law index of the density perturbation spectrum after inflation (Ns) — require an enormous amount of data and compute power to estimate. Using the CosmoFlow model, the research team showed that the CosmoFlow CNN could estimate the values of Ωm and σ8 to the same level of accuracy as existing experiments, and Ns significantly better than previous uses of deep learning for estimation (5x less error than previous measurements).

This is all well and good as far as the cosmology is concerned, but equally exciting were the breakthroughs in the use of large-scale supercomputing that enabled these scientific results.

Without the power of a supercomputer, this work simply could not be performed. Most deep learning exploration today is performed on small, single-node systems. In the authors’ estimate you’d need “more than 60 days of execution time on a single node to converge to a model at an acceptable loss.” The CosmoFlow run using 8,192 nodes “took roughly 9 minutes total with 8 minutes of training time.”

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Core Research And Development

Background

"Supercomputers are unique in their ability to be instruments of discovery for problems on the smallest and largest of scales — from subatomic scale to the cosmos. Cosmologists who study the origin, evolution and eventual fate of the universe use a combination of empirical observations and theoretical computer simulations to define and refine a model of our understanding of the universe. At the core of the model are cosmological parameters describing the global dynamics of the universe, many of which can only be estimated through simulation models — and now deep learning."

Reported Results

"Using the CosmoFlow model, the research team showed that the CosmoFlow CNN could estimate the values of Ωm and σ8 to the same level of accuracy as existing experiments, and Ns significantly better than previous uses of deep learning for estimation (5x less error than previous measurements)."

Benefits

Technology

"CosmoFlow is built as a 3D convolutional neural network using large 3D datasets (generated by N-body simulations) — a combination usually avoided in typical projects using 3D data — where 3D data is converted to 2D because of the compute and I/O demands of 3D."

Data

3D data converted to 2D