Pub Date : 2023-11-01DOI: 10.22369/issn.2153-4136/14/2/1
Elizabeth Bautista, Nitin Sukhija
Today’s job market has its challenges in gaining proficient staff but more so in the High Performance Computing area and within a government lab. Competition from industry in terms of the type of perks they provide, being able to negotiate a higher salary and opportunities of remote work all play a part in losing candidates. At the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL), a site reliability engineer manages the data center onsite 24x7. Further, the facility itself is a unique and complex ecosystem that uses evaporative cooling and recycling of hot air to keep the facility cool. This is in addition to the normal areas to be monitored like the computational systems, the three tier storage, as well as infrastructure and cybersecurity. To explore creating interest into HPC and STEM within the disadvantaged communities near the Laboratory, NERSC partnered with a community college during the pandemic to support high school seniors and freshmen students to provide an educational foundation. In collaboration with the community college, they created a program of specific classes that students needed to take to prepare them for an HPC and/or STEM internships. In certain demographics, students do not believe they can be successful in science or math and require support from the program such as tutors to help them through. With this type of support, students have successfully completed their classes with passing grades. As part of their recruitment process for site reliability engineers to continue to support diversity initiatives at the Laboratory, NERSC implemented an apprenticeship program. This paper describes the current work that includes partnering with a community college program and then NERSC provides a summer internship for the student so they can gain hands-on experience. The first cohort of students have graduated into their internship programs this summer. This paper demonstrates early results from this partnership and how it has impacted the diverse pool of candidates at NERSC.
{"title":"Creating Pathways in Disadvantaged Communities Towards STEM and HPC","authors":"Elizabeth Bautista, Nitin Sukhija","doi":"10.22369/issn.2153-4136/14/2/1","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/2/1","url":null,"abstract":"Today’s job market has its challenges in gaining proficient staff but more so in the High Performance Computing area and within a government lab. Competition from industry in terms of the type of perks they provide, being able to negotiate a higher salary and opportunities of remote work all play a part in losing candidates. At the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL), a site reliability engineer manages the data center onsite 24x7. Further, the facility itself is a unique and complex ecosystem that uses evaporative cooling and recycling of hot air to keep the facility cool. This is in addition to the normal areas to be monitored like the computational systems, the three tier storage, as well as infrastructure and cybersecurity. To explore creating interest into HPC and STEM within the disadvantaged communities near the Laboratory, NERSC partnered with a community college during the pandemic to support high school seniors and freshmen students to provide an educational foundation. In collaboration with the community college, they created a program of specific classes that students needed to take to prepare them for an HPC and/or STEM internships. In certain demographics, students do not believe they can be successful in science or math and require support from the program such as tutors to help them through. With this type of support, students have successfully completed their classes with passing grades. As part of their recruitment process for site reliability engineers to continue to support diversity initiatives at the Laboratory, NERSC implemented an apprenticeship program. This paper describes the current work that includes partnering with a community college program and then NERSC provides a summer internship for the student so they can gain hands-on experience. The first cohort of students have graduated into their internship programs this summer. This paper demonstrates early results from this partnership and how it has impacted the diverse pool of candidates at NERSC.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139298646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.22369/issn.2153-4136/14/2/6
A. Jezghani, Jeffrey Young, Vedavyas Mallela, Will Powell
The challenges of HPC education span a wide array of targeted applications, ranging from developing a new generation of admin-istrators and facilitators to maintain and support cluster resources and their respective user communities, to broadening the impact of HPC workflows by reaching non-traditional disciplines and training researchers in the best-practice tools and approaches when using such systems. Furthermore, standard x86 and GPU architectures are becoming untenable to scale to the needs of computational research, necessitating software and hardware co-development on less-familiar processors. While platforms such as Cerebras and SambaNova have matured to include common frameworks such as TensorFlow and PyTorch as well as robust APIs, and thus are amenable to production use cases and instructional material, other systems may lack such infrastructure maturity, impeding all but the most technically inclined developers from being able to leverage the system. We present here our efforts and outcomes of providing a co-development and instructional platform for the Lucata Pathfinder thread-migratory system in the Rogues Gallery at Georgia Tech. Through a collection of user workflow management, co-development with the platform’s engineers, community tutorials, undergraduate coursework, and student hires, we have been able to explore multiple facets of HPC education in a unique way that can serve as a viable template for others seeking to develop similar efforts.
{"title":"Multifaceted Approaches for Introducing a Hardware-Thread Migratory Architecture","authors":"A. Jezghani, Jeffrey Young, Vedavyas Mallela, Will Powell","doi":"10.22369/issn.2153-4136/14/2/6","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/2/6","url":null,"abstract":"The challenges of HPC education span a wide array of targeted applications, ranging from developing a new generation of admin-istrators and facilitators to maintain and support cluster resources and their respective user communities, to broadening the impact of HPC workflows by reaching non-traditional disciplines and training researchers in the best-practice tools and approaches when using such systems. Furthermore, standard x86 and GPU architectures are becoming untenable to scale to the needs of computational research, necessitating software and hardware co-development on less-familiar processors. While platforms such as Cerebras and SambaNova have matured to include common frameworks such as TensorFlow and PyTorch as well as robust APIs, and thus are amenable to production use cases and instructional material, other systems may lack such infrastructure maturity, impeding all but the most technically inclined developers from being able to leverage the system. We present here our efforts and outcomes of providing a co-development and instructional platform for the Lucata Pathfinder thread-migratory system in the Rogues Gallery at Georgia Tech. Through a collection of user workflow management, co-development with the platform’s engineers, community tutorials, undergraduate coursework, and student hires, we have been able to explore multiple facets of HPC education in a unique way that can serve as a viable template for others seeking to develop similar efforts.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139304123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.22369/issn.2153-4136/14/2/7
Gil Speyer, Neal Woodbury, Arun Neelicattu, Aaron Peterson, Greg Schwimer, George Slessman
A joint proof-of-concept project between Arizona State University and CR8DL, Inc., deployed a Jupyter-notebook based interface to datacenter resources for a computationally intensive, semester-length biochemistry course project. Facilitated for undergraduate biochemistry students with limited high-performance computing experience, the straightforward interface allowed for large scale computations. As the project progressed, various enhancements were identified and implemented.
{"title":"Orchestrating Cloud-supported Workspaces for a Computational Biochemistry Course at Large Scale","authors":"Gil Speyer, Neal Woodbury, Arun Neelicattu, Aaron Peterson, Greg Schwimer, George Slessman","doi":"10.22369/issn.2153-4136/14/2/7","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/2/7","url":null,"abstract":"A joint proof-of-concept project between Arizona State University and CR8DL, Inc., deployed a Jupyter-notebook based interface to datacenter resources for a computationally intensive, semester-length biochemistry course project. Facilitated for undergraduate biochemistry students with limited high-performance computing experience, the straightforward interface allowed for large scale computations. As the project progressed, various enhancements were identified and implemented.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139301578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.22369/issn.2153-4136/14/2/4
S. Mehringer, Kate Cahill, John-Paul Navarro, Scott Lathrop, Charlie Dey, Mary Thomas, Jeaime H. Powell
{"title":"Assessing Shared Material Usage in the High Performance Computing (HPC) Education and Training Community","authors":"S. Mehringer, Kate Cahill, John-Paul Navarro, Scott Lathrop, Charlie Dey, Mary Thomas, Jeaime H. Powell","doi":"10.22369/issn.2153-4136/14/2/4","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/2/4","url":null,"abstract":"","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.22369/issn.2153-4136/14/2/2
Richard Lawrence, Zhenhua He, Dhruva K. Chakravorty, Wesley Brashear, Honggao Liu, S. Nite, Lisa M. Perez, Chris P. Francis, Nikhil Dronamraju, Xin Yang, Taresh Guleria, Jeeeun Kim
Computing programs for secondary school students are rapidly becoming a staple at High Performance Computing (HPC) centers and Computer Science departments around the country. Developing curriculum that targets specific computing subfields with unmet needs remains a challenge. Here, we report on developments in the two week Summer Computing Academy (SCA) to focus on two such subfields. During the first week, ‘Computing for a Better Tomor-row: Data Sciences’, introduced students to real-life applications of big data processing. A variety of topics were covered, including genomics and bioinformatics, cloud computing, and machine learning. During the second week, ‘Camp Secure: Cybersecurity’, focused on issues related to principles of cybersecurity. Students were taught online safety, cryptography, and internet structure. The two weeks are unified by a common thread of Python programming. Modules from the SCA program may be implemented at other institutions with relative ease and promote cybertraining efforts nationwide.
面向中学生的计算课程正迅速成为全国各地高性能计算（HPC）中心和计算机科学系的主要课程。针对尚未满足需求的特定计算子领域开发课程仍然是一项挑战。在此，我们报告了为期两周的夏季计算学院（SCA）的发展情况，重点关注两个这样的子领域。在第一周，"Computing for a Better Tomor-row：数据科学 "向学生们介绍了大数据处理在现实生活中的应用。涉及的主题多种多样，包括基因组学和生物信息学、云计算和机器学习。第二周是 "安全营"：网络安全营 "侧重于与网络安全原则有关的问题。学生们学习了网络安全、密码学和互联网结构。Python 编程是这两周的共同主线。SCA 项目中的模块可在其他机构轻松实施，并促进全国的网络培训工作。
{"title":"Cybersecurity and Data Science Curriculum for Secondary Student Computing Programs","authors":"Richard Lawrence, Zhenhua He, Dhruva K. Chakravorty, Wesley Brashear, Honggao Liu, S. Nite, Lisa M. Perez, Chris P. Francis, Nikhil Dronamraju, Xin Yang, Taresh Guleria, Jeeeun Kim","doi":"10.22369/issn.2153-4136/14/2/2","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/2/2","url":null,"abstract":"Computing programs for secondary school students are rapidly becoming a staple at High Performance Computing (HPC) centers and Computer Science departments around the country. Developing curriculum that targets specific computing subfields with unmet needs remains a challenge. Here, we report on developments in the two week Summer Computing Academy (SCA) to focus on two such subfields. During the first week, ‘Computing for a Better Tomor-row: Data Sciences’, introduced students to real-life applications of big data processing. A variety of topics were covered, including genomics and bioinformatics, cloud computing, and machine learning. During the second week, ‘Camp Secure: Cybersecurity’, focused on issues related to principles of cybersecurity. Students were taught online safety, cryptography, and internet structure. The two weeks are unified by a common thread of Python programming. Modules from the SCA program may be implemented at other institutions with relative ease and promote cybertraining efforts nationwide.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139294769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.22369/issn.2153-4136/14/1/9
Samuel Biggerstaff, Jennifer L. Muzyka, David Toth
{"title":"Computational Analysis of SARS-CoV-2 Therapeutics Development","authors":"Samuel Biggerstaff, Jennifer L. Muzyka, David Toth","doi":"10.22369/issn.2153-4136/14/1/9","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/1/9","url":null,"abstract":"","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130130856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.22369/issn.2153-4136/14/1/4
Bálint Gyires-Tóth, Işıl Öz, Joe Bungo
Researchers and developers in a variety of fields have benefited from the massively parallel processing paradigm. Numerous tasks are facilitated by the use of accelerated computing, such as graphics, simulations, visualisations, cryptography, data science, and machine learning. Over the past years, machine learning and in particular deep learning have received much attention. The development of such solutions requires a different level of expertise and insight than that required for traditional software engineering. Therefore, there is a need for novel approaches to teaching people about these topics. This paper outlines the primary challenges of accelerated computing and deep learning education, discusses the methodology and content of the NVIDIA Deep Learning Institute, presents the results of a quantitative survey conducted after full-day workshops, and demonstrates a sample adoption of DLI teaching kits for teaching heterogeneous parallel computing.
{"title":"Teaching Accelerated Computing and Deep Learning at a Large-Scale with the NVIDIA Deep Learning Institute","authors":"Bálint Gyires-Tóth, Işıl Öz, Joe Bungo","doi":"10.22369/issn.2153-4136/14/1/4","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/1/4","url":null,"abstract":"Researchers and developers in a variety of fields have benefited from the massively parallel processing paradigm. Numerous tasks are facilitated by the use of accelerated computing, such as graphics, simulations, visualisations, cryptography, data science, and machine learning. Over the past years, machine learning and in particular deep learning have received much attention. The development of such solutions requires a different level of expertise and insight than that required for traditional software engineering. Therefore, there is a need for novel approaches to teaching people about these topics. This paper outlines the primary challenges of accelerated computing and deep learning education, discusses the methodology and content of the NVIDIA Deep Learning Institute, presents the results of a quantitative survey conducted after full-day workshops, and demonstrates a sample adoption of DLI teaching kits for teaching heterogeneous parallel computing.","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124017706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.22369/issn.2153-4136/14/1/3
Izumi Barker, Mozhgan Kabiri Chimeh, Kevin Gott, T. Papatheodore, Mary P. Thomas
{"title":"Approaching Exascale: Best Practices for Training a Diverse Workforce using Hackathons","authors":"Izumi Barker, Mozhgan Kabiri Chimeh, Kevin Gott, T. Papatheodore, Mary P. Thomas","doi":"10.22369/issn.2153-4136/14/1/3","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/1/3","url":null,"abstract":"","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127775412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.22369/issn.2153-4136/14/1/2
Bo Lu, David Lampert
{"title":"Python-Based Tools for Modeling Transport in Porous Media Columns","authors":"Bo Lu, David Lampert","doi":"10.22369/issn.2153-4136/14/1/2","DOIUrl":"https://doi.org/10.22369/issn.2153-4136/14/1/2","url":null,"abstract":"","PeriodicalId":330804,"journal":{"name":"The Journal of Computational Science Education","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115032759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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