High-Performance Single-Particle Imaging Reconstruction on Pre-Exascale Computing Platforms*

Single-particle imaging (SPI) reconstruction is the analysis of X-ray diffraction patterns from a light source directed at biological molecules. SPI uses intense X-ray free electron laser (FEL) pulses to obtain a continuous diffraction pattern from single molecules in a serial manner--one molecule at a time The analysis is computationally intensive, requiring diffraction patterns to be sorted into conformations and oriented in 3D for model building and refinement. SPI is also data-intensive, requiring potentially hundreds of thousands of diffraction patterns in order to produce a high-resolution result.

The ExaFEL project is an Exascale Computing Project with the goal of performing SPI reconstruction in quasi-real time where the analysis keeps up with experimental data rates. Upgraded detectors and light sources such as the Linac Coherent Light Source (LCLS) are delivering SPI experimental data at ever higher velocities and larger volumes.

This talk will overview the capabilities of SpiniFEL, the high-performance analytics code for SPI reconstruction developed by the ExaFEL team. SpiniFEL implements the multi-tiered iterative phasing (M-TIP) algorithm for SPI reconstruction. SpiniFEL has been accelerated via GPU offloading as well as through parallel and distributed programming models. It can run using the Message Passing Interface (MPI) or via the task-based Legion Runtime System. SpiniFEL runs on the pre-exascale machines Summit at the Oak Ridge Leadership Computing Facility (OLCF) and Perlmutter at the National Energy Research Scientific Computing Center (NERSC).

We will present results of SpiniFEL runs on pre-exascale computing platforms and outline how SpiniFEL SPI analysis fits into the larger vision of an inter-facility workflow that moves data from acquisition at an experimental facility to computation at a supercomputing center, so scientists can quickly determine structures and produce high quality results efficiently using scarce experimental resources.