Collaborative Research: Phylanx: Python based Array Processing in HPX

合作研究：Phylanx：HPX 中基于 Python 的数组处理

• 批准号: 1737785
• 负责人: Hartmut Kaiser
• 金额: $ 37.32万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737785&HistoricalAwards=false
• 关键词: Collaborative; Research; Phylanx; Python; based

The availability and size of data sets has increased significantly over the course of the past decade. To enable the analysis of large data sets on High Performance Computing (HPC) resources while minimizing time- and energy-to-solution requires incorporating static and runtime information to determine the best possible data layout of the large data arrays used by an application to minimize data movement. The goal of this proposal is to deliver Phylanx, a general purpose framework supporting a variety of data science, machine learning, and statistically oriented applications. Phylanx is designed such that a user?s code will be able to perform efficiently on current and future architecture as long as the runtime system is maintained. This greatly reduces the maintenance burden and will increase the productivity of domain scientists. Phylanx lays a solid foundation for technology transfer from academia to industry and fills the gap between academic innovation and commercial application, by creating a software layer that industrial partners can feel confident relying upon. Phylanx is a scalable, array-based and distributed framework targeting HPC systems using the HPX, dynamic asynchronous task-based parallel runtime system. The dataflow-style capabilities exposed by HPX guarantee the preservation of all data-dependencies even for complex distributed workflows. This project overcomes some of the limitations of existing Big Data solutions such as Hadoop, Spark, and Flink by providing users the ability to: implement NumPy-styled expression-graphs using Python or C/C++, optimize these graphs for optimal data layout, distribution, tiling, and minimal communication overheads, and evaluate those graphs with high efficiency on a runtime interpreter targeting distributed HPC systems. Additionally, Phylanx uses greedy sub-modular techniques on the expression tree to provide a mathematically provable guarantee of optimal performance in machine learning domains and in data placement problems. The platform will provide implementations of 6 benchmarks which have been selected for their domain specificity in text, image, and graph applications.

在过去十年中，数据集的可用性和规模显著增加。为了能够在高性能计算（HPC）资源上分析大型数据集，同时最大限度地减少解决方案所需的时间和精力，需要结合静态和运行时信息，以确定应用程序使用的大型数据阵列的最佳数据布局，从而最大限度地减少数据移动。该提案的目标是交付Phylanx，这是一个通用框架，支持各种数据科学、机器学习和面向统计的应用程序。Phylanx的设计使用户？只要运行时系统得到维护，S代码将能够在当前和未来的架构上有效地执行。这将大大减少维护负担，并将提高领域科学家的生产力。Phylanx为从学术界到工业界的技术转移奠定了坚实的基础，通过创建一个行业合作伙伴可以放心依赖的软件层，填补了学术创新与商业应用之间的空白。Phylanx是一个可扩展的，基于阵列的分布式框架，目标是HPC系统，使用HPX，基于动态异步任务的并行运行时系统。HPX暴露的数据流风格功能保证了所有数据依赖的保存，即使是复杂的分布式工作流。该项目克服了现有大数据解决方案（如Hadoop、Spark和Flink）的一些局限性，为用户提供了以下能力：使用Python或C/ c++实现numpy风格的表达式图，优化这些图以获得最佳的数据布局、分布、平铺和最小的通信开销，并在针对分布式HPC系统的运行时解释器上高效地评估这些图。此外，Phylanx在表达式树上使用贪婪子模块技术，为机器学习领域和数据放置问题的最佳性能提供数学上可证明的保证。该平台将提供6个基准测试的实现，这些基准测试是根据它们在文本、图像和图形应用程序中的特定领域而选择的。

项目成果

Integration of CUDA Processing within the C++ Library for Parallelism and Concurrency (HPX)

将 CUDA 处理集成到 C 库中以实现并行性和并发性 (HPX)

• DOI: 10.1109/espm2.2018.00006
• 发表时间: 2018
• 期刊: 2018 IEEE/ACM 4th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2
• 作者: Diehl, Patrick;Seshadri, Madhavan;Heller, Thomas;Kaiser, Hartmut
• 通讯作者: Kaiser, Hartmut

Runtime Adaptive Task Inlining on Asynchronous Multitasking Runtime Systems

异步多任务运行时系统上的运行时自适应任务内联

• DOI: 10.1145/3337821.3337915
• 发表时间: 2019
• 期刊: ICPP 2019 Proceedings of the 48th International Conference on Parallel Processing
• 作者: Wagle, Bibek;Monil, Mohammad Alaul;Huck, Kevin;Malony, Allen D.;Serio, Adrian;Kaiser, Hartmut
• 通讯作者: Kaiser, Hartmut

An Introduction to hpxMP: A Modern OpenMP Implementation Leveraging HPX, An Asynchronous Many-Task System

hpxMP 简介：利用异步多任务系统 HPX 的现代 OpenMP 实现

• DOI: 10.1145/3318170.3318191
• 发表时间: 2019
• 期刊: IWOCL'19 Proceedings of the International Workshop on OpenCL
• 作者: Zhang, Tianyi;Shirzad, Shahrzad;Diehl, Patrick;Tohid, R.;Wei, Weile;Kaiser, Hartmut
• 通讯作者: Kaiser, Hartmut

Methodology for Adaptive Active Message Coalescing in Task Based Runtime Systems

基于任务的运行时系统中自适应主动消息合并的方法

• DOI: 10.1109/ipdpsw.2018.00173
• 发表时间: 2018
• 期刊: 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
• 作者: Wagle, Bibek;Kellar, Samuel;Serio, Adrian;Kaiser, Hartmut
• 通讯作者: Kaiser, Hartmut

Asynchronous Execution of Python Code on Task-Based Runtime Systems

• DOI: 10.1109/espm2.2018.00009
• 发表时间: 2018-10
• 期刊: 2018 IEEE/ACM 4th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2)
• 作者: R. Tohid;Bibek Wagle;S. Shirzad;Patrick Diehl;Adrian Serio;Alireza Kheirkhahan;Parsa Amini;Katy Williams;Kate Isaacs;K. Huck;Steven R. Brandt;Hartmut Kaiser