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Distributed Continuous GPU Profiling
Trace GPU performance issues from kernel stalls or memory bottlenecks—directly to the PyTorch code, CUDA kernels, native code, or scheduler threads that launched them—with zero friction
NVIDIA Nsight Compute provides good code introspection but comes at a steep cost: heavyweight, intrusive, clunky interfaces and outputs that practically require a PhD in GPU architecture to decipher. Whether you're running custom PyTorch models or serving inference via vLLM, Ollama, or llama.cpp, zymtrace creates a unified view that connects accelerator execution with host orchestration logic— bridging the gap that has historically made GPU optimization so challenging. Detecting stall reasons when GPUs sit idle Identifying CPU bottlenecks that impact GPU utilization Providing end-to-end visibility across the entire compute pipeline
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