Understanding AlphaFold: A Programmer's View
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Most Deep Learning Systems/Compilers in 2021 are still trying to optimize workloads like ResNet & VGG, though these models are really useful and we can publish papers on decent conference by proposing new compiler techniques for them (e.g. TASO/PET). I'm not so enthusiastic about competing on this track, Graph Neural Networks brings structured data into Deep Learning, which is good, but unfortunately the application of large scale GNNs are mostly limited to recommendation systems.
I started surveying the evolving Deep Learning workloads this semester and try to find the "future" of Machine Learning Systems. Currently I feel like Protein Folding and Differentiable Rendering as two applications that worth investigating into.
In this article I'll try to decompose the workflow of AlphaFold2/RoseTTAFold and understand what's the challenges in terms of computing.