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Workload decomposition strategies for shared memory parallel systems with openMP

机译:使用openMP的共享内存并行系统的工作负载分解策略

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A crucial issue in parallel programming (both for distributed and shared memory architectures) is work decomposition. Work decomposition task can be accomplished without large Programming effort with use of high-level parallel program- Ming languages, such as OpenMP. Anyway particular care Must still be payed on achieving performance goals. In this Paper we introduce and compare two decomposition strate- gies, in the framework of shared memory systems, as applied To a case study particle in cell application.
机译:并行编程(对于分布式和共享内存体系结构)的关键问题是工作分解。通过使用高级并行程序-Ming语言(例如OpenMP),无需进行大量编程工作就可以完成工作分解任务。无论如何,必须特别注意实现绩效目标。在本文中,我们在共享存储系统的框架中介绍并比较了两种分解策略,这些策略适用于单元格应用中的案例研究粒子。

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