Deep learning hardware achieves high throughput and low power consumption by reducing computing precision and specializing in matrix multiplication. For machine learning inference, fixed-point value computation is commonplace, where the input and output values and the model parameters are quantized. Thus, many processors are now equipped with fast integer matrix multiplication units (IMMU). It is of significant interest to find a way to harness these IMMUs to improve the performance of HPC applications while maintaining accuracy. We focus on the Ozaki scheme, which computes a high-precision matrix multiplication by using lower-precision computing units, and show the advantages and disadvantages of using IMMU. The experiment using integer Tensor Cores shows that we can compute double-precision matrix multiplication faster than cuBLAS and an existing Ozaki scheme implementation on FP16 Tensor Cores on NVIDIA consumer GPUs. Furthermore, we demonstrate accelerating a quantum circuit simulation by up to 4.33 while maintaining the FP64 accuracy.

翻译：深度学习硬件通过降低计算精度并专门化矩阵乘法运算来实现高吞吐量和低功耗。在机器学习推理中，定点值计算（输入输出值及模型参数均经量化处理）已成为常态。因此，许多处理器现已配备快速整数矩阵乘法单元（IMMU）。如何利用这些IMMU在保持精度的前提下提升高性能计算应用的性能，成为重要研究课题。本文聚焦于Ozaki方案（一种利用低精度计算单元实现高精度矩阵乘法的技术），分析了使用IMMU的优缺点。基于整数Tensor Cores的实验表明，在NVIDIA消费级GPU上，我们不仅能比cuBLAS和现有基于FP16 Tensor Cores的Ozaki方案实现更快的双精度矩阵乘法，还能在保持FP64精度的同时，将量子电路模拟加速高达4.33倍。