  The accuracy of AlphaFold2, a frontier end-to-end structure prediction
system, is already close to that of the experimental determination techniques.
Due to the complex model architecture and large memory consumption, it requires
lots of computational resources and time to train AlphaFold2 from scratch.
Efficient AlphaFold2 training could accelerate the development of life science.
In this paper, we propose a Parallel Evoformer and Branch Parallelism to speed
up the training of AlphaFold2. We conduct sufficient experiments on UniFold
implemented in PyTorch and HelixFold implemented in PaddlePaddle, and Branch
Parallelism can improve the training performance by 38.67% and 36.93%,
respectively. We also demonstrate that the accuracy of Parallel Evoformer could
be on par with AlphaFold2 on the CASP14 and CAMEO datasets. The source code is
available on https://github.com/PaddlePaddle/PaddleFleetX
