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prototype_source/max_autotune_on_CPU_tutorial.rst

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Introduction
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``max-autotune`` mode for the Inductor CPU backend in ``torch.compile`` (`RFC link <https://github.com/pytorch/pytorch/issues/125683>`)
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``max-autotune`` mode for the Inductor CPU backend in ``torch.compile`` (`RFC link <https://github.com/pytorch/pytorch/issues/125683>`_)
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profiles multiple implementations of operations at compile time and selects the best-performing one,
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trading longer compilation times for improved runtime performance. This enhancement is particularly beneficial for GEMM-related operations.
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In the Inductor CPU backend, we’ve introduced a C++ template-based GEMM implementation as an alternative to the ATen-based approach that relies on oneDNN and MKL libraries.

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