Neural architecture search (NAS), which automates the design of neural network (NN) architectures for scientific datasets, requires significant computational resources and time — often on the order of days or weeks of GPU hours and training time. We design the Analytics for Neural Network (A4NN) workflow, a composable workflow that significantly reduces the time and resources required to design accurate and efficient NN architectures. We introduce a parametric fitness prediction strategy and distribute training across multiple accelerators to decrease the aggregated NN training time. A4NN rigorously record neural architecture histories, model states, and metadata to reproduce the search for near-optimal NNs. We demonstrate A4NN’s ability to reduce training time and resource consumption on a dataset generated by an X-ray Free Electron Laser (XFEL) experiment simulation. When deploying A4NN, we decrease training time by up to 37% and epochs required by up to 38%.

Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks.

Neural networks (NNs) are increasingly utilized in high-throughput scientific workflows. In this context, NN efficiency is essential for successful workflow management. We use a multi-objective Neural Architecture Search (NAS), NSGA-Net, to search for highly accurate NNs while optimizing for efficient use of computational resources by minimizing FLoating-point Operations Per Second (FLOPS). We define a domain-agnostic methodology to generate NNs with the support of NSGA-Net, select promising NNs that balance accuracy and FLOPS usage, and refine a subset of NNs in order to curate networks suitable for efficient data analysis. We apply this methodology to a protein diffraction use case. Preliminary results show NNs that efficiently classify conformation of proteins with a final accuracy of 97.7% or higher and using only 187 FLOPS.

Neural networks (NN) are used in high-performance computing and high-throughput analysis to extract knowledge from datasets. Neural architecture search (NAS) automates NN design by generating, training, and analyzing thousands of NNs. However, NAS requires massive computational power for NN training. To address challenges of efficiency and scalability, we proposePENGUIN, a decoupled fitness prediction engine that informs the search without interfering in it.PENGUINuses parametric modeling to predict fitness of NNs. Existing NAS methods and parametric modeling functions can be plugged intoPENGUINto build flexible NAS workflows. Through this decoupling and flexible parametric modeling,PENGUINreduces training costs: it predicts the fitness of NNs, enabling NAS to terminate training NNs early. Early termination increases the number of NNs that fixed compute resources can evaluate, thus giving NAS additional opportunity to find better NNs. We assess the effectiveness of our engine on 6,000 NNs across three diverse benchmark datasets and three state of the art NAS implementations using the Summit supercomputer. Augmenting these NAS implementations withPENGUINcan increase throughput by a factor of 1.6 to 7.1. Furthermore, walltime tests indicate thatPENGUINcan reduce training time by a factor of 2.5 to 5.3.