Deep Learning Performance Optimization From First Principles
Original: Making deep learning go brrrr from first principles (2022)
Why This Matters
Provides systematic approach to GPU optimization as compute-memory gap widens
Technical guide explains deep learning performance optimization through three key bottlenecks: compute utilization, memory bandwidth, and overhead. Author Horace He breaks down systematic approach to GPU optimization instead of relying on ad-hoc tricks.
The article presents a systematic framework for optimizing deep learning model performance, moving beyond random optimization tricks. Author identifies three fundamental performance bottlenecks: compute (actual GPU floating point operations), memory (tensor transfers within GPU), and overhead (everything else). The piece emphasizes maximizing compute-bound regimes to fully utilize expensive GPU resources like 312 teraflops. A key challenge highlighted is the growing gap between compute capability growth and memory bandwidth improvement rates, making it increasingly difficult to achieve peak GPU efficiency. The author uses factory analogy - sending instructions (overhead) and materials (memory bandwidth) to keep the factory (compute) running efficiently. Understanding which regime your system operates in allows targeted optimization rather than guesswork.