Performance Enhancement of the Ozaki Scheme on Integer Matrix Multiplication Unit
<|reference_start|>Performance Enhancement of the Ozaki Scheme on Integer Matrix Multiplication Unit: This study was aimed at simultaneously achieving sufficient accuracy and high performance for general matrix multiplications. Recent architectures, such as NVIDIA GPUs, feature high-performance units designed for low-precision matrix multiplications in machine learning models, and next-generation architectures are expected to follow the same design principle. The key to achieving superior performance is to fully leverage such architectures. The Ozaki scheme, a highly accurate matrix multiplication algorithm using error-free transformations, enables higher-precision matrix multiplication to be performed through multiple lower-precision matrix multiplications and higher-precision matrix additions. Ootomo et al. implemented the Ozaki scheme on high-performance matrix multiplication units with the aim of achieving both sufficient accuracy and high performance. This paper proposes alternative approaches to improving performance by reducing the numbers of lower-precision matrix multiplications and higher-precision matrix additions. Numerical experiments demonstrate the accuracy of the results and conduct performance benchmarks of the proposed approaches. These approaches are expected to yield more efficient results in next-generation architectures.<|reference_end|>
@article{uchino2024performance,
    title={Performance Enhancement of the Ozaki Scheme on Integer Matrix
  Multiplication Unit},
    author={Yuki Uchino, Katsuhisa Ozaki, Toshiyuki Imamura},
    journal={arXiv preprint arXiv:2409.13313},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13313},
    primaryClass={cs.DC}
}