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The accuracy of AlphaFold2, a frontier end-to-end structure predictionsystem, is already close to that of the experimental determination techniques.Due to the complex model architecture and large memory consumption, it requireslots 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 speedup the training of AlphaFold2. We conduct sufficient experiments on UniFoldimplemented in PyTorch and HelixFold implemented in PaddlePaddle, and BranchParallelism can improve the training performance by 38.67% and 36.93%,respectively. We also demonstrate that the accuracy of Parallel Evoformer couldbe on par with AlphaFold2 on the CASP14 and CAMEO datasets. The source code isavailable on https://github.com/PaddlePaddle/PaddleFleetX

Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism