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17
CRYO-EM PERFORMANCE
The purpose of this study was to validate the optimized Relion (for REgularised LIkelihood OptimizatioN) on Dell EMC PowerEdge
C6420s with Skylake CPUs. Relion was developed from the Scheres lab at MRC Laboratory of Molecular Biology. It uses an empirical
Bayesian approach to refine multiple 3D images or 2D class averages for the data generated from CryoElectron Microscopy (Cryo-EM).
The impressive performance gain from Intel
®
’s efforts in the collaboration of Relion development team reduced the performance gap
between CPUs and GPUs. The CPU/GPU performance comparison results are not shown here; however, the performance gap
becomes single digit fold between Skylake CPU systems and Broadwell CPU/Tesla P100 GPU systems.
Essentially, Cryo-EM is a type of Transmission Electron Microscopy (TEM) for imaging frozen-hydrated specimens at cryogenic
temperatures. Specimens remain in their native state without the need for dyes or fixatives, allowing the study of fine cellular structures,
viruses and protein complexes at molecular resolution. A rapid vitrification at cryogenic temperature is the key step to avoid water
molecule crystallization and forming amorphous solid that does almost no damage to the sample structure. Regular electron
microscopy requires samples to be prepared in complex ways, and the sample preparations make hard to retaining the original
molecular structures. Cryo-EM is not perfect like X-ray crystallography; however, it has quickly gained the popularity in the research
community due to the simple sample preparation steps and flexibility of the sample size, complexity, and non-rigid structure. As the
resolution revolution in Cryo-EM progresses due to the 40+ years of dedicated work from the structural biology community, we now can
yield accurate, detailed 3D models of intricate biological structures at the sub-cellular and molecular scales.
The tests were performed on 8 nodes of Dell PowerEdge C6420s which is a part of Dell EMC Ready Bundle for HPC Life Sciences.
Dell EMC PowerEdge C6420 shows that it is an ideal compute platform for the Optimized Relion. It scales well over various number of
compute nodes with Plasmodium ribosome data. In the future study, we plan to use a larger protein data and more compute nodes to
accomplish more comprehensive scaling tests.
Figure 17 Optimized Relion Benchmark