Many scientific computing problems can be reduced to Matrix-Matrix Multiplications (MMM), making the General Matrix Multiply (GEMM) kernels in the Basic Linear Algebra Subroutine (BLAS) of interest to the high-performance computing community. However, these workloads have a wide range of numerical requirements. Ill-conditioned linear systems require high-precision arithmetic to ensure correct and reproducible results. In contrast, emerging workloads such as deep neural networks, which can have millions up to billions of parameters, have shown resilience to arithmetic tinkering and precision lowering.

翻译：许多科学计算问题可归结为矩阵-矩阵乘法（MMM），这使得基本线性代数子程序（BLAS）中的通用矩阵乘法（GEMM）内核备受高性能计算领域的关注。然而，这些工作负载对数值精度有着广泛的需求：病态线性系统需要高精度算术以确保结果正确且可重现；而诸如具有数百万至数十亿参数的深度神经网络等新兴工作负载，则展现出对算术调整和精度降低的鲁棒性。