habaneroupc++:一个无需编译器的PGAS库

Vivek Kumar, Yili Zheng, Vincent Cavé, Zoran Budimlic, Vivek Sarkar
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引用次数: 47

摘要

分区全局地址空间(PGAS)编程模型结合了共享和分布式内存特性,为高性能和高生产率并行编程环境提供了基础。upc++[39]是最近的PGAS实现,它采用基于库的方法,避免了与编译器转换相关的复杂性。然而,该实现不支持动态任务并行性,而仅依赖于其他线程模型(例如,OpenMP或pthreads)来利用PGAS位置中的并行性。在本文中,我们介绍了一个名为habaneroupc++的无编译器PGAS库,它比标准的混合编程方法支持更紧密的位置内和位置间并行性集成。该库在其api中大量使用c++ 11 lambda函数。c++ 11 lambdas避免了对编译器支持的需要,同时仍然保留了基于语言的方法的语法便利性。habaneroupc++库的实现是基于upc++库和habaneroc++库的紧密集成,并使用新的扩展来支持集成。upc++库用于使用GASNet提供PGAS通信和函数传递支持,habanero - c++库用于提供集成了函数传递的本地工作窃取支持。我们使用两个扩展到6K核的基准测试来演示我们的实现的可编程性和性能。本文提出的见解有望进一步提高PGAS编程模型的可用性和普及程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HabaneroUPC++: a Compiler-free PGAS Library
The Partitioned Global Address Space (PGAS) programming models combine shared and distributed memory features, providing the basis for high performance and high productivity parallel programming environments. UPC++ [39] is a very recent PGAS implementation that takes a library-based approach and avoids the complexities associated with compiler transformations. However, this implementation does not support dynamic task parallelism and only relies on other threading models (e.g., OpenMP or pthreads) for exploiting parallelism within a PGAS place. In this paper, we introduce a compiler-free PGAS library called HabaneroUPC++, which supports a tighter integration of intra-place and inter-place parallelism than standard hybrid programming approaches. The library makes heavy use of C++11 lambda functions in its APIs. C++11 lambdas avoid the need for compiler support while still retaining the syntactic convenience of language-based approaches. The HabaneroUPC++ library implementation is based on a tight integration of the UPC++ library and the Habanero-C++ library, with new extensions to support the integration. The UPC++ library is used to provide PGAS communication and function shipping support using GASNet, and the Habanero-C++ library is used to provide support for intra-place work-stealing integrated with function shipping. We demonstrate the programmability and performance of our implementation using two benchmarks, scaled up to 6K cores. The insights developed in this paper promise to further enhance the usability and popularity of PGAS programming models.
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