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Protein structure prediction helps to understand gene translation and proteinfunction, which is of growing interest and importance in structural biology.The AlphaFold model, which used transformer architecture to achieveatomic-level accuracy in protein structure prediction, was a significantbreakthrough. However, training and inference of the AlphaFold model arechallenging due to its high computation and memory cost. In this work, wepresent FastFold, an efficient implementation of AlphaFold for both trainingand inference. We propose Dynamic Axial Parallelism and Duality AsyncOperations to improve the scaling efficiency of model parallelism. Besides,AutoChunk is proposed to reduce memory cost by over 80% during inference byautomatically determining the chunk strategy. Experimental results show thatFastFold reduces overall training time from 11 days to 67 hours and achieves7.5X - 9.5X speedup for long-sequence inference. Furthermore, we scale FastFoldto 512 GPUs and achieve an aggregate throughput of 6.02 PetaFLOP/s with 90.1%parallel efficiency.

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours