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arXiv · 2511.13764

Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels

Arun Thangamani·Md Asghar Ahmad Shahid·Adam Siemieniuk·Rolf Morel·Renato Golin·Alexander Heinecke
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Abstract

The rapidly evolving landscape of AI and machine learning workloads has widened the gap between high-level domain operations and efficient hardware utilization. Achieving near-peak performance still demands deep hardware expertise-experts either handcraft target-specific kernels (e.g., DeepSeek) or rely on specialized libraries (e.g., CUTLASS)-both of which add complexity and limit scalability for most ML practitioners. This paper introduces a compilation scheme that automatically generates scalable, high-performance microkernels by leveraging the MLIR dialects to bridge domain-level operations and processor capabilities. Our approach removes dependence on low-level libraries by enabling the compiler to auto-generate near-optimal code directly. At its core is a mechanism for composing nanokernels from low-level IR constructs with near-optimal register utilization, forming efficient microkernels tailored to each target. We implement this technique in an MLIR-based compiler supporting both vector and tile based CPU instructions. Experiments show that the generated nanokernels are of production-quality, and competitive with state-of-the-art microkernel libraries.

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Arun Thangamani, Md Asghar Ahmad Shahid, Adam Siemieniuk, Rolf Morel, Renato Golin, Alexander Heinecke. 2025-11-14. Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels. https://arxiv.org/abs/2511.13764

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