基于混合精度的新一代神威处理器张量数学运算并行优化

Shuwei Fan, Yao Liu, Juliang Su, Xianyou Wu, Qiong Jiang
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引用次数: 0

摘要

张量数学运算作为高性能计算(HPC)应用的重要组成部分,对应用性能有着广泛而重要的影响。然而,由于新一代神威处理器独特的异构架构和软件环境,利用处理器的计算能力进行张量数学运算至关重要。现有的研究没有充分考虑张量数学运算的计算特点和新一代神威处理器的硬件特点。本文提出了一种在新一代神威处理器上进行张量数学运算的优化方法。首先,提出了一种优化初等函数的方法,实现了高性能的变精度矢量初等函数。然后,提出了一种混合精度优化方法,根据用户的精度要求实现变精度表达式的计算。最后,提出了一种多级并行优化方法,实现了主核和从核的异步并行。实验结果表明,与原生实现相比,优化后的张量数学运算在64核上的平均加速可达到112.19倍,超过理论加速。
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Mixed Precision Based Parallel Optimization of Tensor Mathematical Operations on a New-generation Sunway Processor
As an important part of high-performance computing (HPC) applications, tensor mathematical operations have a wide and significant impact on application performance. However, due to the unique heterogeneous architecture and software environment of the new-generation Sunway processors, it is critical to utilize the computing capacities of the processor for tensor mathematical operations. The existing research has not fully considered the computing characteristics of tensor mathematical operations and the hardware features of the new-generation Sunway processor. In this paper, we propose an optimization method for tensor mathematical operations on the new-generation Sunway processor. Firstly, an optimization method for elementary functions is proposed, which implements high-performance vector elementary functions with variable precision. Then, an mixed precision optimization method is proposed, which realizes expression computation with variable precision according to precision requirements of users. Finally, a multi-level parallel optimization method is proposed, which realizes asynchronous parallelism of the master core and the slave cores. The experimental results show that, compared with the native implementation, optimized tensor mathematical operations can achieve an average speedup of 112.19× on 64 cores, which exceeds the theoretical speedup.
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