CKTI: A Domain-Specific Compiler for Lowering CUDA Kernels to Triton-IR
CUDA kernels are essential for high-performance computing, yet their deployment has been limited to vendor-specific chips. Unless highly efficient computational kernels are custom-implemented by experts, other chips may face problems such as low utilization and inability to accelerate AI computing. In this paper, we introduce CKTI, a domain-specific compiler that lowers CUDA kernels to Triton-IR, thereby decoupling them from proprietary hardware and fostering diversity across the computing ecosystem. CKTI proposes a scheduling algorithm that transforms the threading model from thread-level to tile-level, along with a mapping scheme from explicit control-structure characteristics to dynamic masks. It also incorporates a custom dialect to express the complete semantics and optimizable properties of the kernel. These collectively ensure performance portability and cross-platform deployment. The results show that CKTI produces correct outputs on different hardware platforms and delivers competitive performance, achieving 1.28X speedup on NVIDIA, 1.17X on AMD, 1.14X on MetaX, and unlocking deployment on Cambricon platforms. Additionally, we validate CKTI’s support for end-to-end workloads across multiple architecture, including NPUs and GPGPUs.