# Scaling and Acceleration of Three-dimensional Structure Determination for Single-Particle Imaging Experiments with SpiniFEL

arXiv: https://arxiv.org/abs/2109.05339 · HTML: https://mlsanity.com/paper/2109.05339/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs](https://mlsanity.com/paper/2102.08463/) (2021)

## Built on by (most cited first, 2 of 2)

- [SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples](https://mlsanity.com/paper/2302.06895/) (2023)
- [Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning](https://mlsanity.com/paper/2311.16652/) (2023)

## Abstract (arXiv)

The Linac Coherent Light Source (LCLS) is an X- ray free electron laser (XFEL) facility enabling the study of the structure and dynamics of single macromolecules. A major upgrade will bring the repetition rate of the X-ray source from 120 to 1 million pulses per second. Exascale high performance computing (HPC) capabilities will be required to process the corresponding data rates. We present SpiniFEL, an application used for structure determination of proteins from single-particle imaging (SPI) experiments. An emerging technique for imaging individual proteins and other large molecular complexes by outrunning radiation damage, SPI breaks free from the need for crystallization (which is difficult for some proteins) and allows for imaging molecular dynamics at near ambient conditions. SpiniFEL is being developed to run on supercomputers in near real-time while an experiment is taking place, so that the feedback about the data can guide the data collection strategy. We describe here how we reformulated the mathematical framework for parallelizable implementation and accelerated the most compute intensive parts of the application. We also describe the use of Pygion, a Python interface for the Legion task-based programming model and compare to our existing MPI+GPU implementation.