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A meta-predictor framework for prefetching in object-based DSMs

机译:在基于对象的DSM中预取的元预测器框架

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Dynamic optimizers modify the binary code of programs at runtime by profiling and optimizing certain aspects of the execution. We present a completely software-based framework that dynamically optimizes programs for object-based distributed shared memory (DSM) systems on clusters. In DSM systems, reducing the number of messages between cluster nodes is crucial. Prefetching transfers data in advance from the storage node to the local node so that communication is minimized. Our framework uses a profiler and a dynamic binary rewriter that monitor the access behavior of the application and place prefetches where they are beneficial to speed up the application. In addition, we use two distinct predictors to handle different types of access patterns. A meta-predictor analyzes the memory access behavior and dynamically enables one of the predictors. Our system also adapts the number of prefetches per request to best fit the application's behavior. The evaluation shows that the performance of our system is better than the manual prefetching. The number of messages sent decreases by up to 90%. Performance gains of up to 80% can be observed on benchmarks.
机译:动态优化器通过分析和优化执行的某些方面,在运行时修改程序的二进制代码。我们提出了一个完全基于软件的框架,该框架可动态优化群集上基于对象的分布式共享内存(DSM)系统的程序。在DSM系统中,减少群集节点之间的消息数量至关重要。预取将数据预先从存储节点传输到本地节点,从而使通信量最小化。我们的框架使用一个探查器和一个动态二进制重写器来监视应用程序的访问行为,并将预取放置在有利于加速应用程序的位置。另外,我们使用两个不同的预测变量来处理不同类型的访问模式。元预测器分析内存访问行为并动态启用其中一个预测器。我们的系统还会调整每个请求的预取次数,以最适合应用程序的行为。评估表明,我们的系统的性能优于手动预取。发送的邮件数量最多减少90%。在基准测试中可以看到高达80%的性能提升。

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