Tensor cores (TCs) are a type of Application-Specific Integrated Circuit (ASIC) and are a recent addition to Graphics Processing Unit (GPU) architectures. As such, TCs are purposefully designed to greatly improve the performance of Matrix Multiply-Accumulate (MMA) operations. While TCs are heavily studied for machine learning and closely related fields, where their high efficiency is undeniable, MMA operations are not unique to these fields. More generally, any computation that can be expressed as MMA operations can leverage TCs, and potentially benefit from their higher computational throughput compared to other general-purpose cores, such as CUDA cores on Nvidia GPUs. In this paper, we propose the first double precision (FP64) Euclidean distance calculation algorithm, which is expressed as MMA operations to leverage TCs on Nvidia GPUs, rather than the more commonly used CUDA cores. To show that the Euclidean distance can be accelerated in a real-world application, we evaluate our proposed TC algorithm on the distance similarity self-join problem, as the most computationally intensive part of the algorithm consists of computing distances in a multi-dimensional space. We find that the performance gain from using the tensor core algorithm over the CUDA core algorithm depends weakly on the dataset size and distribution, but is strongly dependent on data dimensionality. Overall, TCs are a compelling alternative to CUDA cores, particularly when the data dimensionality is low ($\leq{4}$), as we achieve an average speedup of $1.28\times$ and up to $2.23\times$ against a state-of-the-art GPU distance similarity self-join algorithm. Furthermore, because this paper is among the first to explore the use of TCs for FP64 general-purpose computation, future research is promising.
翻译:塔岩核心(TCs) 是应用特殊度集成电路的一种类型, 是图像处理股(GPU)结构的最近新增。 因此, TCs 的目的设计目的就是要大大改进矩阵乘积( MMA) 操作的性能。 虽然TCs 是为机器学习和密切相关的字段进行的大量研究, 其效率是不可否认的, MMA 操作并不是这些领域独有的。 更一般地说, 任何可以表现为 MMA 操作能够利用 TC( ASIC) 的计算结果, 并且有可能从它们与其他通用核心( 如 CUDA Nvidia GPS 上的 CUDA 核心数据) 相比更高的计算结果中获益。 在本文中, 我们提出第一个双精度计算( FP64 Euclidea) 远程计算算法( FP64 Euclideidean commlational ) 算算算算法( ), 以MMMA 操作方式在 Nvidiadia GUDA 中, 以更常用的普通的离子为主。 在实际应用中可以加速的距离中, 显示Eucli- daldeal- dal- daldeal- daldealde 数据中, 我们在计算中, 的计算中, 的计算中, 。