Author

Kang-Kang Chen

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Book Open access Sep 2026

TileGEMM: Boosting the Performance of GEMM on AMX-Powered CPUs by Exploiting Data Reuse

TileGEMM is proposed, a high-performance GEMM implementation on AMX that systematically enhances data reuse across the memory hierarchy, and achieves average speedups of 3.27 × and 1.96 × over AVX-512-based implementations TVM and MKL, respectively.

Kang-Kang Chen, Hua-You Su, Meng-Han Jia et al. · Proceedings of the Internati... · 0 citations

#large language models Book Open access Sep 2026

TileGEMM: Boosting the Performance of GEMM on AMX-Powered CPUs by Exploiting Data Reuse

General Matrix Multiplication (GEMM) is the cornerstone of high-performance computing and deep learning. Its efficiency significantly influences the performance of applications ranging from large language models to scientific simulations. Intel Advanced Matrix Extensions (AMX) significantly boost matrix operations thro...

Kang-Kang Chen, Hua-You Su, Menghan Jia et al. · Proceedings of the Internati... · 0 citations