An adaptive cut-off for task parallelism
An adaptive cut-off for task parallelism
复制标题
DOI：
10.1145/1413370.1413407
复制
发表时间：
2008-11
期刊：
影响因子：
--
通讯作者：
A. Duran;J. Corbalán;E. Ayguadé
中科院分区：
文献类型：
--
作者：
A. Duran;J. Corbalán;E. Ayguadé
在任务并行语言中，实现良好性能的一个重要因素是使用截断技术来减少所创建任务的数量。使用截断来避免任务数量过多有助于运行时系统减少与任务创建相关的总开销，特别是当任务是细粒度的时候。不幸的是，最佳截断技术通常取决于应用程序结构，甚至是应用程序的输入数据。我们提出一种新的截断技术，它利用在运行时从应用程序收集的信息来决定应该删减哪些任务以提高应用程序的性能。这种技术不依赖程序员来确定最适合应用程序的截断技术。我们已经在新的OpenMP任务模型环境中实现了这种截断。我们通过各种应用程序进行的评估表明，我们的自适应截断能够做出良好的决策，并且大多数时候与程序员手动设置的最佳截断相匹配。
In task parallel languages, an important factor for achieving a good performance is the use of a cut-off technique to reduce the number of tasks created. Using a cut-off to avoid an excessive number of tasks helps the runtime system to reduce the total overhead associated with task creation, particularlt if the tasks are fine grain. Unfortunately, the best cut-off technique its usually dependent on the application structure or even the input data of the application. We propose a new cut-off technique that, using information from the application collected at runtime, decides which tasks should be pruned to improve the performance of the application. This technique does not rely on the programmer to determine the cut-off technique that is best suited for the application. We have implemented this cut-off in the context of the new OpenMP tasking model. Our evaluation, with a variety of applications, shows that our adaptive cut-off is able to make good decisions and most of the time matches the optimal cut-off that could be set by hand by a programmer.