Exploring Handwritten PTX Code for GPU Optimization in CUDA

Nvidia experts highlight the growing interest in GPU optimization techniques as accelerated computing gains traction in AI and scientific computing. Developers can choose from a spectrum of tools, from high-level frameworks to low-level assembly languages like Parallel Thread Execution (PTX).

While libraries such as CUDA-X simplify GPU programming for quantum computing and data processing, direct coding in C++, Fortran, or Python remains an option. Handwritten PTX, though rare, offers fine-grained control for performance-critical sections—albeit at the cost of portability across GPU architectures.

The CUTLASS library exemplifies practical applications of these optimizations. Yet, the trade-off between performance gains and development complexity demands careful consideration.