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Type feedback vs. concrete type inference: a comparison of optimization techniques for object-oriented languages

机译:类型反馈与具体类型推断:面向对象语言的优化技术比较

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Two promising optimization techniques for object-oriented languages are type feedback (profile-based receiver class prediction) and concrete type inference (static analysis). We directly compare the two techniques, evaluating their effectiveness on a suite of 23 SELF programs while keeping other factors constant.Our results show that both systems inline over 95% of all sends and deliver similar overall performance with one exception: SELF's automatic coercion of machine integers to arbitrary-precision integers upon overflow confounds type inference and slows down arithmetic-intensive benchmarks.We discuss several other issues which, given the comparable run-time performance, may influence the choice between type feedback and type inference.
机译:面向对象语言的两种有希望的优化技术是类型反馈(基于配置文件的接收器类别预测)和具体类型推断(静态分析)。我们直接比较了这两种技术,并在保持其他因素不变的情况下,在一套23个SELF程序中评估了它们的有效性。我们的结果表明,这两个系统内联了95%以上的所有发送并提供了相似的总体性能,但有一个例外:SELF的机器自动强制溢出时将整数转换为任意精度整数会​​混淆类型推断并减慢算法密集型基准测试。我们讨论了其他几个问题,这些问题在可比的运行时性能下可能会影响类型反馈和类型推断之间的选择。

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