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Chaudhary, N.; Yang, W.; Kalamkar, D.; Zhou, J.; Ghosh, S.; Xia, L.; Tiwari, M.; Heinecke, A.; Kaul, B.; Misra et al.
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(R) Xeon(R) CPU. By leveraging the CPUs 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. Code availabilityBaremetal: https://github.com/IntelLabs/open-omics-alphafold Containerized: https://github.com/IntelLabs/Open-Omics-Accelera tion-Framework/tree/main/pipelines/alphafold2-based-protein-folding
In a cheeky tech rebellion, Intel researchers show humble Xeon CPUs can outpace fancy H100 GPUs at AlphaFold2 protein folding with a 1.76x speedup, flipping the "GPUs rule everything" script like an underdog CPU victory dance.
Thrilled announcement by Sanchit Misra (@sanchit_misra) drew HPC and computational biology interest for cost-effective alternatives
View discussion on XPeer review in progress...
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CD4⁺ T cells confer transplantable rejuvenation via Rivers of telomeres
Lanna, A.; Valvo, S.; Dustin, M.; Rinaldi, F.
Using a GPT-5-driven autonomous lab to optimize the cost and titer of cell-free protein synthesis
Smith, A. A.; Wong, E. L.; Donovan, R. C.; Chapman, B. A.; Harry, R.; Tirandazi, P.; Kanigowska, P.; Gendreau, E. A.; Dahl, R. H.; Jastrzebski, M.; Cortez, J. E.; Bremner, C. J.; Hemuda, J. C. M.; Dooner, J.; Graves, I.; Karandikar, R.; Lionetti, C.; Christopher, K.; Consiglio, A. L.; Tran, A.; McCusker, W.; Nguyen, D. X.; Nunes da Silva, I. B.; Bautista-Ayala, A. R.; McNerney, M. P.; Atkins, S.; McDuffie, M.; Serber, W.; Barber, B. P.; Thanongsinh, T.; Nesson, A.; Lama, B.; Nichols, B.; LaFrance, C.; Nyima, T.; Byrn, A.; Thornhill, R.; Cai, B.; Ayala-Valdez, L.; Wong, A.; Che, A. J.; Thavaraj
A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons
Heffel, M. G.; Xu, H.; Pastor-Alonso, O.; Li, X.; Baig, M. S.; Irfan Ghoor, R.; Li, R.; Kern, C.; Kum, J.; Zhang, Y.; Paino, J.; Tsai, M. J.; Tai, C.-Y.; Tucker, G.; Zhao, Z.; Hou, A.; von Behren, Z.; Bhade, M.; Li, S.; Sandoval, K.; Scholes, J.; Codrea, F.; Calimlim, J.; Liao, E. K.; Leung, G.; Kim, J.; Eskin, E.; Flint, J.; Cotter, J. A.; Pasaniuc, B.; Bintu, B.; Zhu, Q.; Mukamel, E. A.; Ernst, J.; Paredes, M. F.; Luo, C.
Prediction of transformative breakthroughs in biomedical research
Davis, M. T.; Busse, B. L.; Arabi, S.; Meyer, P.; Hoppe, T. A.; Meseroll, R. A.; Hutchins, B. I.; Willis, K. A.; Santangelo, G. M.