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The composites performance team at the Air Force Research Laboratory Materials and Manufacturing Directorate uses a combination of novel and high-impact experiments, in-house high-fidelity HPC simulation software, and machine learning to characterize and predict the performance of current and emerging materials.
Exquisite supercomputers have been the target of high-fidelity scientific simulation for the past three decades, due to their extreme throughput, interconnect, and I/O. However, Kubernetes clusters designed for machine learning tasks offer significantly greater computational capacity than high-end workstations, though significantly less than a "Top500" system. This project explores the challenges and suitability of using containerized Kubernetes jobs to perform communication-heavy scientific simulations at various scales.
What will I be doing?
This project will provide useful insights that may impact future plans for acquiring and building computational systems at AFRL, such as, 1) high-end workstations with a few GPUs for research scientists/engineers and 2) modest, in-house HPC systems with high-performance interconnect (InfiniBand) and I/O. Outside of AFRL, the DoD HPCMP provides the highest-end systems available at the time. However, the increasing importance of machine learning (ML) and ML-specific workloads have prompted a few groups in AFRL to invest in performant Kubernetes clusters composed of many GPUs and many-core compute nodes, albeit with a lower investment into the interconnect and I/O compared to exquisite HPC systems. These Kubernetes clusters provide significantly more compute capacity than a single workstation can provide, software portability/reproducibility through containerization, job scheduling infrastructure, and scalability. They leverage the massive advances made for enterprise computing and blur the lines between the workstation/in-house HPC/DoD HPC segmentation of compute capacity that is followed at AFRL. Performant enterprise-like clusters can critically meet the needs of the ML community and flexibly fill in the continuum of compute capacity between a workstation and a DoD HPC system. This project aims to explore the challenges and establish guidelines for using these clusters for distributed scientific simulation workloads that were designed for DoD HPC systems.
Under the guidance of a mentor, you will research in a range of activities typical of a research scientist using simulation tools. You will consist of an update meeting where they will present a few slides summarizing progress and receive guidance from the mentors, additional technical meetings as needed to troubleshoot issues that arise, documenting processes and configurations, creating simulation input files, creating Volcano job .yml files, and measuring performance under varying compute conditions. Any missing skills will be learned throughout the summer using a combination of direct meetings/lectures with the mentor and guided reading/exercises. Throughout the project you will exercise technical communication skills by writing a final report and presenting your research to the composites research team at AFRL, gain experience running simulation software designed for HPC, and become familiar with containerization technologies. Additionally, you will have the opportunity to attend multiple seminars at AFRL each week on topics ranging from machine learning to material science. Tours of laboratories within RX, including those used for material characterization, polymer science, and composite manufacturing, can be arranged if you have an interest in the material science and engineering aspect of the work in AFRL/RX. Finally, you will have regular discussions with multiple researchers at AFRL and various institutions contributing to ACE, including the University of Dayton Research Institute.
Why should I apply? This fellowship provides the opportunity to independently utilize your skills and engage with experts in innovative ideas to move the proposed research forward.
Where will I be located? Wright-Patterson AFB, Ohio
What is the anticipated start date? June 2023 - Exact start dates will be determined at the time of selection and in coordination with the selected candidate.
What is the appointment length? This appointment is a summer research appointment. Appointments may be extended depending on funding availability, project assignment, program rules, and availability of the participant.
What are the benefits? You will receive a stipend to be determined by the sponsor. Stipends are typically based on a participant’s academic standing, discipline, experience, and research facility location. Other benefits may include the following:
About ORISE
This program, administered by Oak Ridge Associated Universities (ORAU) through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and DoD. Participants do not enter into an employee/employer relationship with ORISE, ORAU, DoD or any other office or agency. Instead, you will be affiliated with ORISE for the administration of the appointment through the ORISE appointment letter and Terms of Appointment. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE. For more information, visit the ORISE Research Participation Program at the U.S. Department of Defense.
A candidate should:
- Have some prior experience in technical communication (including school projects)
- Be familiar with software development in some language (Python is preferred)
- Be organized and able to document processes clearly
- Have completed at least a few computer science courses
An understanding of Kubernetes, Volcano, MPI, and InfiniBand would be helpful but certainly not a requirement.
Security Investigation: Applicants should be able to pass a National Agency Check and Inquiries (NACI) security investigation should they be selected and accept the internship offer.
Application Requirements
A complete application consists of:
If you have questions, send an email to USACE@orise.orau.gov. Please list the reference code of this opportunity in the subject line of the email. Please understand that ORISE does not review applications or select applicants; selections are made by the sponsoring agency identified on this opportunity. All application materials should be submitted via the “Apply” button at the bottom of this opportunity listing. Please do not send application materials to the email address above.
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