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POSE: Phase I: Toward a Task-Parallel Programming Ecosystem for Modern Scientific Computing

POSE：第一阶段：迈向现代科学计算的任务并行编程生态系统

基本信息

批准号：
2229304
负责人：
Tsung-Wei Huang
金额：
$ 29.88万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2023-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229304&HistoricalAwards=false
关键词：
POSE Phase Toward Task Parallel

项目摘要

Open-source software systems designed for task-parallel programming have become central to a wide range of modern scientific computing applications, such as machine learning and quantum computing. While decades of research has yielded many open-source task-parallel programming systems, most of them are led by a handful of developers and their impacts do not sustain in the long run. To overcome this challenge, this project proposes scoping activities to establish a route to a long-term sustainable ecosystem for task-parallel programming. These activities build atop the open-source software, Taskflow, a high-performance task-parallel system to streamline the building of complex scientific computing applications.This project starts by discovering an ecosystem based on three increasing applications of Taskflow, quantum computing, circuit design automation, and multimedia. The discovery effort consists of designing showcase software products and organizing workshops to pursue the optimal ecosystem for Taskflow. Then, the project designs a series of developer training programs to engage potential content contributors who can help develop and maintain Taskflow in a community-driven fashion. Finally, the project establishes a transparent and publicly visible governance model to formalize the decision-making process of Taskflow for various elements, including technical contributions, partnership, and security policies. This project lays the foundation for Taskflow to transition to a robust and sustainable system asset for the scientific computing community to quickly respond to emerging parallelism using scalable task-based approaches. Many scoping activities in this project can instill confidence in commercial adoption of Taskflow and grow its partnership with other open-source scientific computing projects to further broaden its impact and enhance sustainability. Also, results produced in this project can be used in classrooms to renovate existing learning materials for high-performance computing and software practice, engaging a diverse group of students in open-source development.The project website is available at https://taskflow.github.io/ which contains the latest news, source code, step-by-step learning materials, and benchmarks. The authors are committed to developing and maintaining the project in the long term.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为任务并行编程设计的开源软件系统已经成为现代科学计算应用的核心，例如机器学习和量子计算。虽然数十年的研究已经产生了许多开源任务并行编程系统，但其中大多数都是由少数开发人员领导的，从长远来看，它们的影响并不持久。为了克服这一挑战，本项目提出了确定活动范围的建议，以建立一个长期可持续的生态系统，用于任务并行的方案编制。这些活动建立在开源软件Taskflow的基础上，Taskflow是一个高性能的任务并行系统，用于简化复杂科学计算应用程序的构建。该项目首先发现了一个基于Taskflow、量子计算、电路设计自动化和多媒体三个日益增长的应用程序的生态系统。发现工作包括设计展示软件产品和组织研讨会，以寻求Taskflow的最佳生态系统。然后，该项目设计了一系列开发人员培训计划，以吸引潜在的内容贡献者，他们可以以社区驱动的方式帮助开发和维护Taskflow。最后，该项目建立了一个透明和公开可见的治理模型，以正式化任务流的各种元素的决策过程，包括技术贡献，合作伙伴关系和安全策略。该项目为Taskflow过渡到一个强大且可持续的系统资产奠定了基础，以便科学计算社区使用可扩展的基于任务的方法快速响应新出现的并行性。该项目中的许多范围界定活动可以为Taskflow的商业采用注入信心，并发展其与其他开源科学计算项目的伙伴关系，以进一步扩大其影响并增强可持续性。此外，在这个项目中产生的结果可以在课堂上使用，以更新现有的学习材料，高性能计算和软件实践，从事开放源码开发的学生群体的多元化。该项目的网站是在https://taskflow.github.io/，其中包含最新的新闻，源代码，一步一步的学习材料，和基准。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Fast STA Graph Partitioning Framework for Multi-GPU Acceleration

DOI：
10.23919/date56975.2023.10137050
发表时间：
2023-04
期刊：
2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
影响因子：
0
作者：
Guannan Guo;Tsung-Wei Huang;Martin D. F. Wong
通讯作者：
Guannan Guo;Tsung-Wei Huang;Martin D. F. Wong

qTask: Task-parallel Quantum Circuit Simulation with Incrementality

DOI：
10.1109/ipdps54959.2023.00080
发表时间：
2022-10
期刊：
2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
影响因子：
0
作者：
Tsung-Wei Huang
通讯作者：
Tsung-Wei Huang

GenFuzz: GPU-accelerated Hardware Fuzzing using Genetic Algorithm with Multiple Inputs

GenFuzz：使用具有多个输入的遗传算法进行 GPU 加速的硬件模糊测试