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Scalar Replacement Considering Branch Divergence
2022
Journal of Information Processing
GPU with the Single Instruction Multiple Data (SIMD) execution model enables a program to work efficiently. However, the efficiency may decrease because of branch divergence that occurs when SIMD threads follow different paths in some branches. Once the divergence occurs, some threads must wait until completion of the execution of the others. Thus, it is important to reduce branch divergence to improve the efficiency of GPU programs. On the other hand, branch divergence may be increased by some
doi:10.2197/ipsjjip.30.164
fatcat:mhuijvktl5arzc7auj67dvs6mu