Cole Ramos

Cole Ramos, Keith Lowery

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.

Zixian Wang, Cole Ramos, Muhammad A. Awad et al.

In recent years, the rapid advancement of deep neural networks (DNNs) has revolutionized artificial intelligence, enabling models with unprecedented capabilities in understanding, generating, and processing complex data. These powerful architectures have transformed a wide range of downstream applications, tackling tasks beyond human reach. In this paper, we introduce Omniwise, the first end-to-end, self-supervised fine-tuning pipeline that applies large language models (LLMs) to GPU kernel performance prediction--a novel use case in performance profiling. Omniwise is model-agnostic and lightweight, achieving strong results even with a small 3B-parameter model. It can predict key performance metrics, including memory bandwidth, cache hit rates, GFLOPs, and arithmetic intensity, directly from kernel code without the need for code execution or profiling tools. Our approach achieves over 90% of predictions within 10% relative error on GPU kernels executed on AMD MI250 and MI300X architectures. In addition to the pipeline, we develop an online inference server and a Visual Studio Code plugin that seamlessly integrate LLM-based performance prediction into developers' workflows.