Kernel-Lens uses Jev to flag CUDA performance issues in VS Code
The author built a VS Code extension called CUDA Kernel-Lens that parses code segments and feeds them with 24 performance metrics to Jev, which picks the matching issue categories and confidence scores.
Took JEV for a spin this weekend and built (read vibed) this CUDA Kernel-Lens extension (works with VSCode). The idea is simple - get performance signals right where you are writing the kernel - before compiling, profiling or asking a coding agent to chime in. It highlights segments where the code is plagued with strided memory access, repeatedly loading reusable operands from the global memory, blocking an MMA operation on a mbar_wait every iteration, and much more. Underneath it extracts the relevant code segments using a simple code parser and feeds that with predefined Kernel metrics (across 24 perf dimensions) to JEV - which selects the matching categories and their confidence scores. Getting these instantaneous insights while coding is pretty neat actually. In this case, I took an intermediate step from Siboehm's CUDA GEMM optimization. This is a blocked shared memory kernel. JEV highlgihted the possible pain points (all addressed in the next step). Give it a try at https://github.com/gauravjain14/kernel-lens-jev