EAGER: Collaborative Research: Democratizing the Teaching of Parallel Computing Concepts

EAGER:协作研究:并行计算概念教学的民主化

基本信息

项目摘要

A study by the U.S. Council of Competitiveness reports that 97% of U.S. businesses rely on the innovative use of high-performance parallel computing (HPC) to maintain their competitive advantage in the global economy. Furthermore, the ubiquity of multi- and many-core processors in modern computing devices, ranging from the small (embedded devices and cell phones) to the medium (laptops and desktops) to the large (data centers and supercomputers), requires that computing educators properly prepare the next generation of computer scientists to reason about problem solving in a parallel way. Unfortunately, companies lament the dearth of a trained workforce that is familiar with parallel computing concepts, thus exacerbating the problem of the technological change and globalization that is potentially leaving the U.S. behind in the world economy. To address this shortcoming, this project extends and transforms the investigators' existing computer science (CS) curriculum to democratize the teaching of parallel computing concepts by leveraging Scratch, an educational programming language shown to be highly effective in teaching computing to children but lacking in explicit parallel programming constructs. Providing a rich visual environment, Scratch enables students to create sophisticated programs just by dragging predefined components into a visual editor. This project enhances Scratch with abstractions for explicit parallel processing with a prototype for evaluation and deployment to ensure broad applicability, facilitate adoption, and assure educational effectiveness. Enhancing Scratch with such parallel programming abstractions enriches the computer science curriculum, without imposing the cognitive overhead and overload typically associated with teaching parallel computing. This project engenders a unique cross-disciplinary collaboration between the fields of parallel computing, software engineering, and education.Broader Significance: This project aims at democratizing the teaching of parallel computing concepts, thus molding the minds of the next generation of computer scientists to effectively realize solutions that take advantage of explicit parallel processing. This educational intervention has the potential to bridge the technological gap that is leaving the U.S. behind in the world economy. The project directly impacts students from middle school to college and even professionals seeking re-training. Students involved in this research receive training in parallel computing, software engineering, and education. Outreach to underrepresented groups includes the continuation of the PIs's efforts in recruiting and retaining female and underrepresented minority students in research and education under the auspices of this project.
美国竞争力理事会的一项研究报告称,97%的美国企业依靠高性能并行计算(HPC)的创新使用来保持其在全球经济中的竞争优势。此外,多核和众核处理器在现代计算设备中无处不在,从小型(嵌入式设备和手机)到中型(笔记本电脑和台式机)再到大型(数据中心和超级计算机),这要求计算教育工作者适当地准备下一代计算机科学家以并行方式解决问题。不幸的是,公司哀叹缺乏熟悉并行计算概念的训练有素的劳动力,从而加剧了技术变革和全球化的问题,这可能使美国在世界经济中落后。为了解决这一缺点,该项目扩展和改造调查人员现有的计算机科学(CS)课程,利用Scratch,教育编程语言是非常有效的教学计算的儿童,但缺乏明确的并行编程结构的并行计算概念的教学民主化。Scratch提供了丰富的视觉环境,使学生能够通过将预定义的组件拖到视觉编辑器中来创建复杂的程序。该项目通过抽象来增强Scratch,用于显式并行处理,并提供用于评估和部署的原型,以确保广泛的适用性,促进采用并确保教育效果。用这种并行编程抽象来增强Scratch丰富了计算机科学课程,而不会强加通常与教授并行计算相关的认知开销和过载。该项目是并行计算、软件工程和教育领域之间独特的跨学科合作。更广泛的意义:该项目旨在使并行计算概念的教学民主化,从而塑造下一代计算机科学家的头脑,以有效地实现利用显式并行处理的解决方案。这种教育干预有可能弥合美国在世界经济中落后的技术差距。该项目直接影响到从中学到大学的学生,甚至是寻求再培训的专业人员。参与这项研究的学生接受并行计算,软件工程和教育方面的培训。与代表性不足的群体的联系包括在该项目的赞助下,继续努力在研究和教育领域招募和留住女性和代表性不足的少数民族学生。

项目成果

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Wuchun Feng其他文献

Wuchun Feng的其他文献

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{{ truncateString('Wuchun Feng', 18)}}的其他基金

Collaborative Research: Workshop Series on Sustainable Computing
协作研究:可持续计算研讨会系列
  • 批准号:
    2125999
  • 财政年份:
    2021
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
RAPID: Higher Accuracy and Availability of COVID-19 Testing and Monitoring via Post-CT Image Boosting and Analysis
RAPID:通过 CT 后图像增强和分析提高 COVID-19 测试和监测的准确性和可用性
  • 批准号:
    2031215
  • 财政年份:
    2020
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
RAPID: A Computational Deep-Learning Approach for Fast, Accurate CT Testing and Monitoring of COVID-19
RAPID:一种计算深度学习方法,可快速、准确地进行 CT 测试和 COVID-19 监测
  • 批准号:
    2027607
  • 财政年份:
    2020
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
Phase-I IUCRC Virginia Tech: Center for Space, High-performance, and Resilient Computing (SHREC)
第一阶段 IUCRC 弗吉尼亚理工大学:空间、高性能和弹性计算中心 (SHREC)
  • 批准号:
    1822080
  • 财政年份:
    2018
  • 资助金额:
    $ 26万
  • 项目类别:
    Continuing Grant
NSF XPS Workshop for Exploiting Parallelism and Scalability
NSF XPS 利用并行性和可扩展性研讨会
  • 批准号:
    1451021
  • 财政年份:
    2014
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
XPS: SDA: Collaborative Research: A Scalable and Distributed System Framework for Compute-Intensive and Data-Parallel Applications
XPS:SDA:协作研究:用于计算密集型和数据并行应用的可扩展分布式系统框架
  • 批准号:
    1337131
  • 财政年份:
    2013
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
BIGDATA: Mid-Scale: DA: Collaborative Research: Genomes Galore - Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing
大数据:中规模:DA:协作研究:基因组丰富 - 高通量 DNA 测序的核心技术、库和领域特定语言
  • 批准号:
    1247693
  • 财政年份:
    2013
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
CiC (RDDC): Commoditizing Data-Intensive Biocomputing in the Cloud
CiC (RDDC):云中数据密集型生物计算的商品化
  • 批准号:
    1048253
  • 财政年份:
    2011
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
MRI-R2: Acquisition of a Heterogeneous Supercomputing Instrument for Transformative Interdisciplinary Research
MRI-R2:获取用于变革性跨学科研究的异构超级计算仪器
  • 批准号:
    0960081
  • 财政年份:
    2010
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
CSR: Small: Collaborative Research: Hybrid Opportunistic Computing for Green Clouds
CSR:小型:协作研究:绿色云的混合机会计算
  • 批准号:
    0916719
  • 财政年份:
    2009
  • 资助金额:
    $ 26万
  • 项目类别:
    Continuing Grant

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