论文标题
使用并行的Evoformer和分支并行性，有效的AlphaFold2训练
Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism
论文作者
论文摘要
Alphafold2的准确性是边境端到端结构预测系统，已经接近实验确定技术的准确性。由于复杂的模型体系结构和大量的内存消耗，因此需要大量的计算资源和时间来训练Alphafold2。有效的AlphaFold2培训可以加速生命科学的发展。在本文中，我们提出了一个平行的进化剂和分支并行性，以加快AlphaFold2的训练。我们对在Paddlepaddle实施的Pytorch和Helixfold实施的Unifold进行了足够的实验，分支平行性可以分别提高训练性能38.67％和36.93％。我们还证明了并行进化器的准确性可以与CASP14和Cameo数据集上的Alphafold2相当。源代码可在https://github.com/paddlepaddle/paddlefleetx上找到
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
