Compiler optimization used manual tuning technique in the past several decades.
过去几十年编译器的优化主要使用的是人工调优的方法。
The experimental results show that, automatic compiler tuning can get better performance than the manual tuning in the training cases and in the new cases, they get close performance.
实验数据表明，基于机器学习的编译自动调优技术对已知实例能得到比手工调优更好的性能，而对未知实例的预测能力也能达到跟手工调优接近的水平。
For performance tuning, you might need to adjust other kernel parameters. The manual procedure is as follows.
在性能调优时，可能还需要调整其他内核参数。
See the operation section of this manual for switch tuning positions.
参考这本手册开关调谐位置的操作部分。
The compiler developer must pay more time on manual performance tuning, and the tuning result usually can not catch up with the development of the computer architecture.
编译器人工调优的方法将会耗费编译器开发者大量的时间，而且调优的结果难以适应计算机体系结构的不断变化。
The compiler developer must pay more time on manual performance tuning, and the tuning result usually can not catch up with the development of the computer architecture.
编译器人工调优的方法将会耗费编译器开发者大量的时间，而且调优的结果难以适应计算机体系结构的不断变化。
应用推荐