Current Python programming environment does not have any reliable and efficient multiple precision floating-point (MPF) arithmetic except ``mpmath" and ``gmpy2" packages based on GNU MP(GMP) and MPFR libraries. Although it is well known that multi-component-type MPF library can be utilized for middle length precision arithmetic under 200 bits, they are not widely used on Python environment. In this paper, we describe our accelerated MPF direct method with AVX2 techniques and its application to mixed precision iterative refinement combined with mpmath, and demonstrate their efficiency on x86\_64 computational environments.

中文翻译：

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Python编程环境下的加速多精度直接法和混合精度迭代细化

除了基于 GNU MP(GMP) 和 MPFR 库的“mpmath”和“gmpy2”包之外，当前的 Python 编程环境没有任何可靠和高效的多精度浮点 (MPF) 算法。虽然众所周知多分量类型的 MPF 库可用于 200 位以下的中等长度精度算法，但它们在 Python 环境中并没有广泛使用。在本文中，我们描述了使用 AVX2 技术的加速 MPF 直接方法及其在与 mpmath 相结合的混合精度迭代细化中的应用，并展示了它们在 x86\_64 计算环境中的效率。