Revisiting CPUs for Protein Folding: Xeon-Based Acceleration of AlphaFold2

Protein structure prediction via AlphaFold2 has revolutionized drug discovery, yet its end-to-end execution remains computationally intensive. While GPUs are traditionally favored for deep learning, the AlphaFold2 algorithm consists of heterogeneous phases — preprocessing with sparse database searches and model inference with low-arithmetic-intensity attention modules — that present unique architectural challenges. In this work, we address these bottlenecks by introducing Open-Omics-AlphaFold2, a highly optimized implementation for Intel ^® Xeon ^® CPU. By leveraging the CPU’s versatility in handling both sparse preprocessing algorithms and dense matrix operations via Intel Advanced Matrix Extensions (AMX), we accelerate the entire pipeline end-to-end. Our optimization strategy employs multi-level parallelism — spanning multiprocessing, multi-threading, and vectorization — alongside cacheaware tiling and operator fusion. Our results demonstrate that, on a Xeon CPU, Open-Omics-AlphaFold2 achieves 2 7.58 speedup for preprocessing and 19.8 29.2 speedup for model inference over baseline Deepmind-AlphaFold2. Moreover, for a proteome of 391 proteins, Open-Omics-AlphaFold2 running on a dual-socket Intel Xeon 6980P system achieves a remarkable 76% higher through-put over the state-of-the-art GPU-accelerated solution, FastFold, running on a single-socket Intel Xeon 6980P CPU with an NVIDIA H100 offioad.