CSR: Small: High-Level Programming Languages and Environments for Scalable Graph Processing

图形和图形算法是计算机科学的基础。尽管从历史上看，它们在传统的科学计算中并没有发挥重要作用，但随着信息学和以数据为中心的应用程序的出现，它们的重要性正在迅速增加。尽管现在许多图形分析任务都是按顺序执行的，但问题的大小仍在继续增长，这就需要越来越多地使用并行计算。并行图算法已经在编写中，但需要付出巨大的努力和有限的代码重用。目前实现并行图算法的一个主要问题是缺乏性能可移植性：不仅经常需要在不同的平台上重新实现算法以获得最佳性能，而且经常需要完全重新设计它们。为了避免这种重写，从而提高科学家的生产力，该项目将研究特定于领域的编程语言，允许图算法被可移植地表达，同时使用编译技术，允许高级表达式获得与这些算法的手写低级代码相媲美的性能。特定于领域的语言已经在多个应用领域显示出优势。该项目将在图形领域进一步扩展它们的优势，包括比过去更多的平台和数据表示。特别是，该项目的目标包括找到不同平台上以及不同应用程序之间的图形应用程序的表达式所共有的抽象。为了证明这些抽象的有效性，该项目包括为各种高性能计算平台创建图形算法的原型实现，并对照可比的低级版本对算法的高级版本进行评估。

Graphs and graph algorithms are fundamental to computer science. Although historically they have not played a major role in traditional scientific computing, their importance is rapidly increasing with the emergence of informatics and data-centric applications. Although many graph analysis tasks are performed sequentially today, problem sizes continue to grow, necessitating the increasing use of parallel computing. Parallel graph algorithms are already being written, but with great effort and limited code reuse. A major issue in current ways of implementing parallel graph algorithms is the lack of performance portability: not only is it often required to reimplement algorithms on different platforms for the best performance, it is frequently also necessary to completely redesign them.To avoid this rewriting and thus increase scientists' productivity, this project will study domain-specific programming languages allowing graph algorithms to be expressed portably, while using compilation techniques that allow the high-level expressions to achieve performance competitive with hand-written, low-level code for these algorithms. Domain-specific languages have already shown benefit in multiple application areas. This project will extend their benefits further in the graph domain, including to more platforms and data representations than in the past. In particular, the project goals include finding abstractions common to the expressions of graph applications on different platforms, as well as between different applications. To demonstrate the effectiveness of these abstractions, the project includes the creation of prototype implementations of graph algorithms for a variety of high-performance computing platforms and the evaluation of the high-level versions of algorithms against comparable low-level versions.