Performance Bottleneck

Performance bottlenecks in various computational tasks, from large language model training to distributed machine learning, hinder efficiency and scalability. Current research focuses on identifying and mitigating these bottlenecks across different layers, including hardware (GPUs, TPUs, CPUs), software (optimizers, data pipelines), and algorithmic design (e.g., parallelization strategies, quantization techniques). Understanding and addressing these limitations is crucial for advancing machine learning, accelerating scientific discovery, and enabling the development of more efficient and powerful applications.