Performance#

Time per forward projection, adjoint and CGLS iteration for a 512³ volume on 1, 4 and 8 GPUs. Run the same workload with examples/benchmark_scaling.py.

Time per forward, adjoint and CGLS iteration on 1, 4 and 8 GPUs, 512^3 volume Time per forward, adjoint and CGLS iteration on 1, 4 and 8 GPUs, 512^3 volume

512³ volume

1 GPU

4 GPUs, 1 node

8 GPUs, 2 nodes

Forward projection

566.79 ms

150.76 ms (3.8×)

81.58 ms (6.9×)

Adjoint (backprojection)

1060.77 ms

279.37 ms (3.8×)

224.04 ms (4.7×)

CGLS iteration

1753.73 ms

468.09 ms (3.7×)

337.39 ms (5.2×)

diffct 2.4.3, FP32, A100 64 GB GPUs; circular cone beam, 360 views, 1024×768 detector, pitch 1.25. Default memory settings, without an imposed allocator cap. Projection times are medians of seven samples after three warmups. CGLS uses three ten-iteration solves after a two-iteration warmup; the median includes initialization and is divided by ten. Raw samples and configuration: scaling.json.