Abstract: The auto-vectorization cost model is an important component of compiler's auto-vectorization optimization.Its role is to evaluate whether the code can achieve performance improvement after applying vectorization transformation.When the cost model is inaccurate,the compiler will apply vectorization transformation with negative benefit,thus reducing the execution efficiency of the program.Aiming at the inaccuracy of the default cost model of GCC compiler,based on Intel Xeon Silver 4214R CPU,an auto-vectorization cost model based on instruction MKS is proposed.The model fully considers the machine mode,operation type and operation intensity of instructions,and uses gradient descent algorithm to automatically search the approximate cost of different instruction types.Single-thread tests are carried out on SPEC2006 and SPEC2017.Experimental results show that the model can reduce the error of benefit estimation.Compared with the vector program generated by the default cost model,the GCC compiler,after adding the MKS cost model,achieves a maximum speedup of 4.72% on the SPEC2006 benchmark and 7.08% on the SPEC2017 benchmark.

Key words: GCC compiler, Auto-vectorization, Cost model, Profit evaluation, Gradient descent