Performance Optimization Techniques of Irregular-Shaped Matrix Multiplication on SW26010P

Matrix multiplication is widely used in the field of scientific and engineering computing, and is the most important optimization object in BLAS. With the development of artificial neural networks, computational fluid mechanics and other fields, irregular-shaped matrix multiplication is rapidly gaining attention. This paper proposes parallelization techniques for irregular-shaped matrix multiplication on SW26010P, a domestic many-core processor deployed in the new generation Sunway supercomputer. Specifically, a parallel algorithm with diversified task partition mapping is designed to improve memory access bandwidth utilization rate based on the hardware characteristics and the data layout of matrix elements. At the same time, based on the hardware assembly lines and vectorized computation and data access instructions, the key computations are abstracted and the corresponding underlying compilation optimizations are performed to improve computational efficiency. And a data-sharing strategy under the RMA point to point communication mechanism is adopted to further reduce the overhead of data access and transmission, and the nested double buffering are used to further improve the performance. Besides, a series of experiments on SW26010P are conducted to determine the optimal number of blocks of different kinds of function parallelization calculation for the purpose of making full use of the hardware platform performance. The experimental results demonstrate that the performance of the irregular-shaped matrix multiplication optimized in this thesis can reach up to 93% of the upper bound of the theoretical performance. Compared with the massive GEMM algorithm implementation, the average performance acceleration of the irregular-shaped matrix multiplication is 5.43 times, and the optimal performance acceleration can reach up to 51.5 times. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.