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精于矩阵乘法，实现了高吞吐量与低功耗。在机器学习推理中，定点数值计算十分常见，其中输入输出值及模型参数均被量化。因此，许多处理器现已配备高速整数矩阵乘法单元（IMMU）。如何利用这些IMMU在保持精度的同时提升HPC应用性能，已成为一个重要的研究方向。我们聚焦于Ozaki方案——该方案通过使用低精度计算单元实现高精度矩阵乘法——并分析了采用IMMU的优缺点。基于整数Tensor Core的实验表明，在NVIDIA消费级GPU上，我们的方案能以比cuBLAS以及现有基于FP16 Tensor Core的Ozaki方案实现更快的双精度矩阵乘法计算。此外，我们展示了在保持FP64精度的同时，将量子电路模拟的速度提升至4.33倍。