Performance Model

Performance modeling aims to accurately predict the execution time and resource utilization of software, particularly complex systems like deep learning models and configurable software. Current research emphasizes developing accurate yet efficient models, focusing on techniques like deep learning, tensor completion, and causal inference, often incorporating both analytical and data-driven approaches to capture the interplay between hardware and software. These models are crucial for optimizing system design, resource allocation, and performance debugging across diverse applications, from large language models to embedded systems, ultimately improving efficiency and reducing development costs.