DeepContext: A Context-aware, Cross-platform, and Cross-framework Tool for Performance Profiling and Analysis of Deep Learning Workloads

Effective performance optimization of deep learning models requires comprehensive profiling across heterogeneous computing environments, yet existing tools fail to bridge the semantic gap between high-level operations and low-level execution. This paper presents DeepContext, a novel profiling system that correlates program contexts across Python code, deep learning frameworks, C/C++ libraries, and GPU execution. DeepContext features a framework-agnostic shim layer that seamlessly correlates the behavior of the deep learning framework with hardware performance metrics. Furthermore, DeepContext provides an automated performance analyzer that offers actionable optimization guidance based on its holistic view of the entire software stack of deep learning applications. DeepContext works for mainstream deep learning frameworks and runs on modern CPU+GPU architectures with low overhead. Our evaluation demonstrates that DeepContext uncovers previously hidden performance bottlenecks in real-world deep-learning applications. Guided by DeepContext, we are able to fix multiple performance issues, achieving speed-ups between 1.06× and 1.66×.