Input-aware Generalized Matrix-Vector Product Algorithm for Adaptative PerformanceOptimization of Hygon DCU

GEMV(generalized matrix-vector multiplication function) is the core component of BLAS(basic linear algebra subroutine) algorithm library,which is widely used in the fields of computer science,engineering computation and mathematical computation.Currently,with the continuous iterative upgrading of the domestic Hygon DCU version,there is also a certain competitive advantage between the Hygon DCU and the traditional GPU manufacturer before.With the continuous expansion of the application field of GEMV,the input characteristics of GEMV also reflect a diversified tendency,in which case,relying on a single optimization method,it is not possible to realize the GEMV algorithm in all inputs of GPU computing platforms in the cases with high performance.Therefore,in this paper,on the basis of traditional optimization means such as access optimization,instruction rearrangement,parallel statute,shared memory,thread scheduling,we propose an input-aware performance adaptive optimization method,which is able to automatically adjust the implementation of the computation kernel according to the different sizes and shapes of the input matrices in order to achieve the optimal performance,and significantly improves the performance of GEMV on a Hygon DCU.Experimental results show that the overall performance of the generalized matrix-vector multiplication algorithm for input awareness implemented in this paper on the Hygon DCU Z100SM is significantly better than the related algorithms in the RocBLAS library,with a maximum performance improvement of 3.020 3 times that of the corresponding algorithms in the RocBLAS library for different matrix input sizes.