GLAF: A Visual Programming and Auto-tuning Framework for Parallel Computing
GLAF: A Visual Programming and Auto-tuning Framework for Parallel Computing
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GLAF：并行计算的可视化编程和自动调优框架
DOI：
10.1109/icpp.2015.95
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wu
中科院分区：
文献类型：
--
作者：
K. Krommydas;Ruchira Sasanka;Wu
过去十年的计算革命已经为大众提供了并行硬件。然而，由于缺乏并行编程专业知识，利用其能力并在适当的水平上引发科学突破的能力仍然是一个挑战。尽管已经提出了不同的解决方案来促进收获并行计算的种子，但大多数解决方案都针对经验丰富的程序员，而忽略了目标受众（如领域专家）的特殊性。本文讨论了实现编程抽象和实现这一受众的集成开发框架的挑战。我们提出了GLAF -一个基于网格的语言和自动并行化，自动调优框架。它的关键元素是其直观的可视化编程界面，该界面试图使领域专家更容易表达和验证算法，并且能够自动生成有效的串行和并行Fortran和C代码，包括潜在的有益代码修改（例如，关于数据布局）。我们发现，上述功能可以帮助新手程序员避免常见的编程陷阱，并提供快速实现。
The past decade's computing revolution has delivered parallel hardware to the masses. However, the ability to exploit its capabilities and ignite scientific breakthrough at a proportionate level remains a challenge due to the lack of parallel programming expertise. Although different solutions have been proposed to facilitate harvesting the seeds of parallel computing, most target seasoned programmers and ignore the special nature of a target audience like domain experts. This paper addresses the challenge of realizing a programming abstraction and implementing an integrated development framework for this audience. We present GLAF -- a grid-based language and auto-parallelizing, auto-tuning framework. Its key elements are its intuitive visual programming interface, which attempts to render expressing and validating an algorithm easier for domain experts, and its ability to automatically generate efficient serial and parallel Fortran and C code, including potentially beneficial code modifications (e.g., With respect to data layout). We find that the above features assist novice programmers to avoid common programming pitfalls and provide fast implementations.