Posts by Sidafa Conde
Performance Profiling on AMD GPUs - Part 7: AI/PyTorch® Edition
- 04 November 2026
This blog, like the previous articles in the profiling guide series
(Part 1,
Part 2,
Part 3,
Part 4,
Part 5,
and Part 6),
applies a systematic profiling workflow to
a GPU-accelerated application. Those posts establish the core loop on
HIP and Fortran applications: baseline timing, kernel hotspots with rocprofv3, rooflines and
hardware counters with rocprof-compute, and whole-system timelines with rocprof-sys. Here we apply
that loop to a PyTorch training workload, where one logical operator can lower to many
GPU kernels and optimization often means changing how the framework dispatches work rather
than rewriting a kernel.