Accelerating the Cryo-EM Structure Determination in RELION on Modern Many-Core CPU

RELION is a widely-used software suite for cryoelectron microscopy (cryo-EM) single-particle analysis (SPA), yet its performance optimization has primarily focused on x86 CPUs and NVIDIA GPUs. In this work, we present the first systematic effort to optimize RELION on modern many-core CPUs. Through detailed performance analysis, we identify critical bottlenecks across RELION's major computational stages. We then apply a set of software- and hardware-aware optimizations, including vectorization optimization, process and thread configurations tuning, algorithm optimization, lock optimization, memory affinity optimization, and computation redundancy optimization. Our optimized version achieves significant speedups and exhibits better scalability than the original RELION across all stages. Notably, it outperforms a single NVIDIA A100 GPU on the complete SPA workflow, achieving a 2.22 × speedup on the SPA dataset and a 1.13 × speedup on the RELION Benchmark dataset. Validation experiments further confirm that our optimizations preserve the reconstruction accuracy, demonstrating the potential of specific CPU architectures as a competitive and efficient platform for cryo-EM data processing.