文件名称:
Automatic Tuning of Compilers Using Machine Learning-Springer(2018).pdf
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上传时间: 2018-03-04
详细说明： The diversity of today’s architectures has forced programmers to spend additional efforts to port and tune their application source code across different platforms. In this process, compilers need additional tuning to generate better code. Recent compilers offer a vast number of multilayered optimizations, capable of targeting different code segments of an application. Choosing the right set of optimizations can significantly impact the performance of the code. This is a challenge made more complicated by the need tofind the best order in which they should be applied given an application. Finding the best ordering is a long-standing problem in compilation research called the phase-ordering problem. The traditional approach for constructing compiler heuristics to solve it simply cannot cope with the enor- mous complexity of choosing the right ordering of optimizations for each code segment in an application. The current research focuses on exploring, studying, and developing an innovative approach to the problem of identifying the compiler optimizations that maximize the performance of a target application. ...展开收缩
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