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Practical Iterative Optimization for the Data Center

机译：数据中心的实用迭代优化

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摘要

Iterative optimization is a simple but powerful approach that searches the best possible combination of compiler optimizations for a given workload. However, iterative optimization is plagued by several practical issues that prevent it from being widely used in practice: a large number of runs are required to find the best combination, the optimum combination is dataset dependent, and the exploration process incurs significant overhead that needs to be compensated for by performance benefits. Therefore, although iterative optimization has been shown to have a significant performance potential, it seldom is used in production compilers. In this article, we propose iterative optimization for the data center (IODC): we show that the data center offers a context in which all of the preceding hurdles can be overcome. The basic idea is to spawn different combinations across workers and recollect performance statistics at the master, which then evolves to the optimum combination of compiler optimizations. IODC carefully manages costs and benefits, and it is transparent to the end user. To bring IODC to practice, we evaluate it in the presence of co-runners to better reflect real-life data center operation with multiple applications co-running per server. We enhance IODC with the capability to find compatible co-runners along with a mechanism to dynamically adjust the level of aggressiveness to improve its robustness in the presence of co-running applications.
机译：迭代优化是一种简单但功能强大的方法，它针对给定的工作负载搜索编译器优化的最佳组合。但是，迭代优化受到一些实际问题的困扰，这些问题阻碍了迭代优化在实践中的广泛使用：需要大量运行才能找到最佳组合，最佳组合取决于数据集，并且勘探过程会产生大量开销，需要通过性能优势得到补偿。因此，尽管已证明迭代优化具有很大的性能潜力，但很少在生产编译器中使用它。在本文中，我们提出了对数据中心（IODC）的迭代优化：我们证明了数据中心提供了可以克服前面所有障碍的环境。基本思想是在工作程序之间产生不同的组合，并在主服务器上收集性能统计信息，然后演变为编译器优化的最佳组合。 IODC认真管理成本和收益，并且对最终用户透明。为了使IODC付诸实践，我们在有联合运行者的情况下对其进行评估，以更好地反映实际数据中心的运行情况，并在每个服务器上同时运行多个应用程序。我们通过查找兼容的共同运行者的能力以及动态调整攻击性水平的机制来增强IODC，以在存在共同运行的应用程序时提高其鲁棒性。

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