Performance Analysis

Performance analysis in scientific computing focuses on evaluating the efficiency and accuracy of algorithms and models across diverse applications, from large language models and federated learning to object detection and medical image segmentation. Current research emphasizes optimizing model architectures (e.g., UNet, YOLOv5, Transformers) and algorithms (e.g., FedAvg, FedSGD) for specific tasks, often incorporating techniques like pruning, quantization, and knowledge distillation to improve resource efficiency. These analyses are crucial for advancing both fundamental understanding of algorithms and for improving the practical deployment of AI and machine learning in various fields, including healthcare, autonomous systems, and high-performance computing.