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Benchmarks of the Leopard-EM package

Identifying freely oriented macromolecules using Two-Dimensional Template Matching (2DTM) is a computationally intensive task since we must compute millions of cross-correlograms on large cryo-EM micrographs. Efficiency of the match_template program is therefore a key consideration going into the Leopard-EM package.

We include some benchmarking results across different GPU hardware to provide an estimate of match_template performance. These results can help guide users in planning out 2DTM analyses of their data using Leopard-EM and serve as a reference for expected performance.

Benchmarking Setup

Leopard-EM includes a benchmarking script at benchmark/benchmark_match_template.py (if downloaded from source) which you can use to determine performance on your own hardware. This script runs the match_template program using the following parameters:

  • Micrograph size: 4096 x 4096 pixels (Falcon 4i) or 5760 x 4092 pixels (K3)
  • Template size: 512 x 512 x 512 pixels
  • Number of defocus planes: 11
  • Variable orientation batch size configurable using --orientation-batch-size

Note that we empirically observe that template size has negligible effect on performance. Total search times are extrapolated from throughput to a full orientation search of ~1.58 million orientations with 13 defocus planes (~20.5 million total cross-correlations).

Version 1.1 benchmarks

Falcon 4i images (4096 x 4096 pixels)

GPU name VRAM Image size Throughput (cross-corr/sec) 2DTM search time (hours)
GeForce 2080 Ti 11 GB 4096×4096 343.0 16.70
RTX 6000 Ada / L40s 48 GB 4096×4096 744.5 7.69
RTX 6000 Blackwell Max-Q 96 GB 4096×4096 1394.7 4.10
A100 40 GB 4096x4096 923.4 6.19
H100 80 GB 4096×4096 1650.8 3.47

K3 images (5760 x 4092 pixels)

GPU name VRAM Image size Throughput (cross-corr/sec) 2DTM search time (hours)
GeForce 2080 Ti 11 GB 5760×4092 217.1 26.40
RTX 6000 Ada / L40s 48 GB 5760×4092 431.7 13.30
RTX 6000 Blackwell Max-Q 96 GB 5760×4092 799.7 7.15
A100 40 GB 5760×4092 530.2 10.79
H100 80 GB 5760×4092 897.9 6.37

K3 image benchmarks

Note that we have not optimized Leopard-EM v1.1 for K3 images in particular. Future versions should include optimizations for non-square images which will improve performance on K3 data.