中文摘要：

线级的推测从不规则的顺序的应用为线级的并行的利用变得更吸引人。但是思索的线程没能到达期望的平行性能是普通的。原因是思索的线程的性能由事实是极其复杂的它不仅由于模糊控制和数据依赖受不了指导编译器的性能评价的不严密，而且取决于内在的硬件配置和程序行为。因此，这份报纸建议一条静态地贪婪、动态地适应的途径让环级的推测动态地在运行时刻决定最好的环水平。它依靠编译器贪婪地选择并且优化所有环候选人，它然后在为环推测的顺序的决心嵌套层次的不同循环的本利的分析上被继续。在运行时刻循环执行预言下面，我们动态地安排并且更新，并且保证最好的循环水平总是是 parallelized 循环推测的顺序。二条不同政策也被检验最大化全面性能。与传统的静态的环选择技术相比，我们的途径能完成可比较或更好的性能。

英文摘要：

Thread-level speculation becomes more attractive for the exploitation of thread-level parallelism from irregular sequential applications. But it is common for speculative threads to fail to reach the expected parallel performance. The reason is that the performance of speculative threads is extremely complicated by the fact that it not only suffers from the imprecision of compiler-directed performance estimation due to ambiguous control and data dependences, but also depends on the underlying hardware configuration and program behaviors. Thus, this paper proposes a statically greedy and dynamically adaptive approach for loop-level speculation to dynamically determine the best loop level at runtime. It relies on the compiler to select and optimize all loop candidates greedily, which are then proceeded on the cost-benefit analysis of different loop nesting levels for the determination of the order of loop speculation. Under the runtime loop execution prediction, we dynamically schedule and update the order of loop speculation, and ensure the best loop level to be always parallelized. Two different policies are also examined to maximize overall performance. Compared with traditional static loop selection techniques, our approach （：an achieve comparable or better performance.