  Thanks to automated cryo-EM and GPU-accelerated processing, single-particle
cryo-EM has become a rapid structure determination method that permits capture
of dynamical structures of molecules in solution, which has been recently
demonstrated by the determination of COVID-19 spike protein in March, shortly
after its breakout in late January 2020. This rapidity is critical for vaccine
development in response to emerging pandemic. This explains why a 2D
classification approach based on multi-reference alignment (MRA) is not as
popular as the Bayesian-based approach despite that the former has advantage in
differentiating structural variations under low signal-to-noise ratio. This is
perhaps because that MRA is a time-consuming process and a modular
GPU-acceleration library for MRA is lacking. Here, we introduce a library
called Cryo-RALib that expands the functionality of CUDA library used by GPU
ISAC. It contains a GPU-accelerated MRA routine for accelerating MRA-based
classification algorithms. In addition, we connect the cryo-EM image analysis
with the python data science stack so as to make it easier for users to perform
data analysis and visualization. Benchmarking on the TaiWan Computing Cloud
(TWCC) container shows that our implementation can accelerate the computation
by one order of magnitude. The library is available at
https://github.com/phonchi/Cryo-RAlib.

Quantitative Methods;Distributed, Parallel, and Cluster Computing;Image and Video Processing