摘要
为提高编译器的自适应性,以应对复杂的体系结构,提出一个结合迭代编译和机器学习的编译框架。编译器可将在优化空间中搜索到的最佳编译选项信息保存到知识库中,并能从知识库中学习获得适合当前程序的最佳编译选项。实例学习算法具有增量式的特点,可有效利用编译过程中积累的数据。通过避免冗余实例入库以及从库中剔除噪声实例,保证学习的精度与效率。
For the purpose of making the compiler more adaptive and dealing with complex architecture, a compiler framework is proposed which combines iterative compilation and instance-based learning. On one hand, the compiler can search the optimization space and save the best compiler options into the knowledge library; on the other hand, the compiler can learn from the library to get the best compiler options for the current program. The incremental algorithm can make full use of the accumulated data of the compilation. The algorithms are proposed which can keep the redundant instance out of the knowledge library and filter the noise from the library.
出处
《计算机工程》
CAS
CSCD
2012年第3期4-6,共3页
Computer Engineering
基金
"核高基"重大专项"支持国产CPU的编译系统及工具链"(2009ZX01036-001-001)
关键词
迭代编译
机器学习
增量式算法
冗余实例
iterative compilation
machine learning
incremental algorithm
redundant instance
