CyberTraining: Pilot: Cross-Layer Training of High-Performance Deep Learning Technologies and Applications for Research Workforce Development in Central Valley
网络培训:试点:高性能深度学习技术和应用程序的跨层培训,用于中央谷研究人员的发展
基本信息
- 批准号:2321123
- 负责人:
- 金额:$ 29.96万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-01 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
High-Performance Computing (HPC) has revolutionized various scientific fields, including climate research, wildlife health, agricultural sciences, and scientific simulations and modeling. With the emergence of HPC-accelerated deep learning (HPC-DL) systems and applications, there is a pressing need for comprehensive cross-layer training materials to educate the research workforce on these advanced technologies. The primary objective of this pilot project is to address this need by providing comprehensive cross-layer HPC-DL training to a wide range of cyberinfrastructure (CI) users. The target audience includes undergraduate and graduate students, postdocs, faculty, and research staff who can benefit from enhanced knowledge and skills in utilizing HPC-DL CI technologies and resources. By equipping them with the necessary training, the project aims to improve their research efficiency and maximize the potential of HPC-DL in their respective fields. In addition, the project has a specific focus on fostering inclusivity and expanding opportunities for underrepresented communities in the Central Valley area of California. This will contribute to the national interest by empowering individuals with the knowledge and skills necessary to excel in the HPC-DL field. This project addresses the critical training needs of the converged HPC-DL field by developing comprehensive training materials, fostering peer consultant programs, conducting workshops, and building an inclusive learning culture. It includes an integration of scientific applications, HPC technologies, and DL in a cross-layer approach. The training program covers several important CI topics, including Remote Direct Memory Access (RDMA), GPU-based distributed computing, Slurm, MPI, and NCCL, which are critical to achieving high performance for HPC-DL workloads. The training will also dive into distributed DL training frameworks such as PyTorch, TensorFlow, and Horovod, enabling participants to effectively leverage these tools for their research. Moreover, the training incorporates practical DL application case studies, offering real-world examples and insights. The short-term goal is to empower individuals with HPC-DL knowledge and cross-layer optimization skills to maximize the utilization of HPC-DL CI resources and improve research efficiency. This project will also examine the effectiveness of practice-central models and HPC-DL-centered workshops in promoting HPC-DL adoption in underrepresented communities. The project's long-term aim is to cultivate a robust research workforce with a deep understanding of HPC-DL CIs. By establishing a learning culture and targeting a significant number of CI users, this project addresses workforce shortages and extends its impact beyond the Central Valley. Through collaborations and the dissemination of open-source training materials, it will contribute to advancing compute- and data-intensive scientific simulations and knowledge discovery.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.
高性能计算(HPC)已经彻底改变了各种科学领域,包括气候研究,野生动物健康,农业科学以及科学模拟和建模。随着HPC加速的深度学习(HPC-DL)系统和应用的出现,迫切需要全面的跨层培训材料来教育研究人员了解这些先进技术。该试点项目的主要目标是通过向广泛的网络基础设施(CI)用户提供全面的跨层HPC-DL培训来满足这一需求。目标受众包括本科生和研究生,博士后,教师和研究人员,他们可以从利用HPC-DL CI技术和资源的增强知识和技能中受益。通过为他们提供必要的培训,该项目旨在提高他们的研究效率,并最大限度地发挥HPC-DL在各自领域的潜力。此外,该项目还特别注重促进包容性,为加州中央谷地区代表性不足的社区扩大机会。这将通过赋予个人在HPC-DL领域出类拔萃所需的知识和技能来促进国家利益。该项目通过开发全面的培训材料,促进同行顾问计划,举办研讨会和建立包容性学习文化,解决了融合HPC-DL领域的关键培训需求。它以跨层的方式集成了科学应用、HPC技术和DL。该培训计划涵盖了几个重要的CI主题,包括远程直接内存访问(RDMA)、基于GPU的分布式计算、Slurm、MPI和NCCL,这些主题对于实现HPC-DL工作负载的高性能至关重要。培训还将深入到分布式DL培训框架,如PyTorch,TensorFlow和Horovod,使参与者能够有效地利用这些工具进行研究。此外,培训还结合了实际的DL应用案例研究,提供了真实的例子和见解。短期目标是使个人拥有HPC-DL知识和跨层优化技能,以最大限度地利用HPC-DL CI资源并提高研究效率。该项目还将检查以实践为中心的模型和以HPC-DL为中心的研讨会在促进代表性不足的社区采用HPC-DL方面的有效性。该项目的长期目标是培养一支对HPC-DL CI有深刻理解的强大研究队伍。通过建立一种学习文化和针对大量的CI用户,该项目解决了劳动力短缺问题,并将其影响扩大到中央谷以外。该奖项反映了NSF的法定使命,并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,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Xiaoyi Lu其他文献
Mechanical robust and self-healing flexible perovskite solar cells with efficiency exceeding 23%
机械%20鲁棒%20和%20自愈%20灵活%20钙钛矿%20太阳能%20电池%20与%20效率%20超越%2023%
- DOI:
10.1007/s11426-024-1954-8 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Yaohua Wang;Ruikun Cao;Yuanyuan Meng;Bin Han;Ruijia Tian;Xiaoyi Lu;Zhenhua Song;Shuncheng Yang;Congda Lu;Chang Liu;Ziyi Ge - 通讯作者:
Ziyi Ge
Slurm-V: Extending Slurm for Building Efficient HPC Cloud with SR-IOV and IVShmem
Slurm-V:使用 SR-IOV 和 IVShmem 扩展 Slurm 以构建高效的 HPC 云
- DOI:
10.1007/978-3-319-43659-3_26 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Jie Zhang;Xiaoyi Lu;Sourav Chakraborty;D. Panda - 通讯作者:
D. Panda
INAM2: InfiniBand Network Analysis and Monitoring with MPI
INAM2:使用 MPI 进行 InfiniBand 网络分析和监控
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
H. Subramoni;A. Augustine;Mark Daniel Arnold;Jonathan L. Perkins;Xiaoyi Lu;Khaled Hamidouche;D. Panda - 通讯作者:
D. Panda
Designing Virtualization-Aware and Automatic Topology Detection Schemes for Accelerating Hadoop on SR-IOV-Enabled Clouds
设计虚拟化感知和自动拓扑检测方案,以在支持 SR-IOV 的云上加速 Hadoop
- DOI:
10.1109/cloudcom.2016.0037 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Shashank Gugnani;Xiaoyi Lu;D. Panda - 通讯作者:
D. Panda
ON THE ISOTROPIC DISTRIBUTION OF BEAM DIRECTIONS
关于光束方向的各向同性分布
- DOI:
- 发表时间:
2000 - 期刊:
- 影响因子:0
- 作者:
L. Papiez;Xiaoyi Lu;M. Langer - 通讯作者:
M. Langer
Xiaoyi Lu的其他文献
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{{ truncateString('Xiaoyi Lu', 18)}}的其他基金
CAREER: Heterogeneity-Enriched Communication for Advancing HPC Systems and Applications
职业:丰富异构性的通信以推进 HPC 系统和应用程序
- 批准号:
2340982 - 财政年份:2024
- 资助金额:
$ 29.96万 - 项目类别:
Standard Grant
Collaborative Research: EAGER: Automating CI Configuration Troubleshooting with Bayesian Group Testing
协作研究:EAGER:使用贝叶斯组测试自动化 CI 配置故障排除
- 批准号:
2333324 - 财政年份:2023
- 资助金额:
$ 29.96万 - 项目类别:
Standard Grant
SPX: Collaborative Research: Memory Fabric: Data Management for Large-scale Hybrid Memory Systems
SPX:协作研究:内存结构:大规模混合内存系统的数据管理
- 批准号:
2132049 - 财政年份:2021
- 资助金额:
$ 29.96万 - 项目类别:
Standard Grant
SPX: Collaborative Research: Memory Fabric: Data Management for Large-scale Hybrid Memory Systems
SPX:协作研究:内存结构:大规模混合内存系统的数据管理
- 批准号:
1822987 - 财政年份:2018
- 资助金额:
$ 29.96万 - 项目类别:
Standard Grant
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