MAKCI: A Multi-Abstraction AI Kernel Cost Instrumentor for Performance Modeling in AI Compilers

The growing diversity of IRs, programming models, and hardware accelerators has dramatically expanded the AI kernel optimization space. While AI-driven systems (e.g., KernelEvolve) can automate kernel generation, the challenge shifts from generating candidates to efficiently exploring and pruning design choices as spaces grow. Hardware profiling and simulation remain essential for validation but are too costly for iterative design-space exploration. Analytical cost models are therefore critical for rapidly evaluating alternatives, guiding optimization, and enabling hardware-software co-design before hardware measurements are available.

We present MAKCI, a Python-native instrumentation framework for analytical performance modeling across abstraction levels. MAKCI instruments Python-native IRs through fine-grained compute and memory primitives, then estimates kernel cost via a concrete-execution tracer — without hardware profiling. This enables developers and AI agents to rapidly explore and prune optimization choices. As a case study, we apply MAKCI to Triton kernels targeting the IBM Spyre AI accelerator and demonstrate accurate cost estimation without hardware access.