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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Autotuning Skeleton-Driven Optimizations for Transactional Worklist Applications
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Autotuning Skeleton-Driven Optimizations for Transactional Worklist Applications

机译：针对事务性工作清单应用程序自动优化骨架驱动的优化

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摘要

Skeleton or pattern-based programming allows parallel programs to be expressed as specialized instances of generic communication and computation patterns. In addition to simplifying the programming task, such well structured programs are also amenable to performance optimizations during code generation and also at runtime. In this paper, we present a new skeleton framework that transparently selects and applies performance optimizations in transactional worklist applications. Using a novel hierarchical autotuning mechanism, it dynamically selects the most suitable set of optimizations for each application and adjusts them accordingly. Our experimental results on the STAMP benchmark suite show that our skeleton autotuning framework can achieve performance improvements of up to 88 percent, with an average of 46 percent, over a baseline version for a 16-core system and up to 115 percent, with an average of 56 percent, for a 32-core system. These performance improvements match or even exceed those obtained by a static exhaustive search of the optimization space.
机译：基于骨架或基于模式的程序设计允许将并行程序表示为通用通信和计算模式的专用实例。除了简化编程任务之外，这种结构良好的程序还适合在代码生成过程中以及在运行时进行性能优化。在本文中，我们提出了一个新的框架框架，该框架透明地选择性能优化并将其应用于事务性工作列表应用程序中。使用新颖的分层自动调整机制，它可以为每个应用程序动态选择最合适的一组优化，并相应地进行调整。我们在STAMP基准套件上的实验结果表明，与16核系统的基准版本相比，我们的骨架自动调整框架可以实现高达88％的性能提升，平均提高46％，平均达到115％。对于32核系统，为56％。这些性能改进达到或什至超过通过静态详尽搜索优化空间获得的性能改进。

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