Nov 24, 2016 · This paper, instead, focuses on an analytical approach to code generation of the Gemm kernel for different architecture.

This paper distill the implementation of the Gemm kernel into an even smaller kernel, an outer-product, and analytically determine how available SIMD ...

Sep 11, 2024 · We codify this approach into a system to automatically generate a high performance SIMD implementation of the Gemm kernel. Experimental results ...

This paper, instead, focuses on an analytical approach to code generation of the Gemm kernel for different architecture, in order to shed light on the details ...

Bibliographic details on Automating the Last-Mile for High Performance Dense Linear Algebra.

Co-authors ; Automating the Last-Mile for High Performance Dense Linear Algebra. RM Veras, TM Low, TM Smith, RA van de Geijn, F Franchetti. ArXiv e-prints, 2016.

Automating the last-mile for high performance dense linear algebra. RM Veras, TM Low, TM Smith, R van de Geijn, F Franchetti. arXiv preprint arXiv:1611.08035 ...

In deep learning, GEMMs and convolutions (which often use GEMM) are always followed by a non-linear activation which is memory-bound. Allowing non-linearity + ...

Nov 7, 2019 · And in-depth overview of the lowest level details is available in the paper Automating the last mile for High Performance Dense Linear Algebra[5] ...

Missing: Last- Mile