Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs
For GPU kernel developers, this tool eliminates the bottleneck of manual kernel isolation, enabling faster iteration in HPC and ML domains.
Kerncap automates extraction and isolation of GPU kernels from large applications, enabling rapid edit-recompile-validate loops. It achieves a 13.6x speedup over traditional workflows, reducing isolation from hours to a single command across six workloads on three AMD architectures.
Iterative GPU kernel tuning is bottlenecked by the scale of the applications that host the kernels. Rapid iteration requires isolating the kernel so it can be edited, recompiled, and validated without rebuilding the full application -- but manual isolation requires reconstructing build flags, dispatch configuration, and runtime inputs by hand, so developers usually settle for slow in-place edits. We present Kerncap, an automated kernel extraction tool that intercepts dispatches at the HSA runtime for both HIP and Triton, bridging Triton's JIT-only metadata into HSA-level capture via a lightweight Python compile-hook shim. Kerncap performs an address-space closure of all device memory -- a virtual-address-faithful snapshot that preserves embedded device pointers without DWARF metadata or pointer chasing -- locates kernel sources, and emits self-contained reproducer projects. HIP reproducers use a Clang VFS overlay for source-level recompilation without modifying the original build system; Triton reproducers are tuning-pinned, binding the captured autotuner configuration into the artifact to preserve the JIT kernel's numerical contract. Across six real-world HIP and Triton workloads spanning traditional HPC and ML domains on three AMD GPU architectures (CDNA2, CDNA3, RDNA3), \textsc{Kerncap} extracts and validates kernels from snapshots ranging from 152~MB to 30~GB -- including a VA-faithful capture of vLLM's Mixture-of-Experts weight pool reached through pointer indirection. On our llama.cpp case study, Kerncap's edit-recompile-validate loop achieves a 13.6x speedup over the traditional workflow, reducing kernel isolation from a multi-hour process to a single command. The resulting reproducers also serve as a substrate for autotuning agents and LLM-driven kernel generators that need rapid, isolated evaluation of candidates.