SULI– MCS – Phann, Makkara – 6.23.26

Argonne National Laboratory • Lemont, IL

7d • Onsite

About The Position

Modern supercomputers such as Argonne's Aurora Exascale machine are large and complex, and comprise so many components that a small failure rate per component translates into an appreciable failure rate for the entire machine. In order for applications to be made resilient in the face of such unavoidable failures, it is necessary to characterize the application failure rate with good precision, and if possible with some specificity regarding the type and hardware usage pattern of applications. This requires combining of various streams of logging information, and nontrivial statistical analysis, possibly supplemented by machine learning techniques. This project targets the study of application failure rates on Aurora, based on such analyses. The student will explore the log data streams to discover effective ways of tracking application failure rates, write code to analyze, categorize, and visualize application interrupts, and help develop models of machine computational efficiency.

Requirements

• Currently enrolled in undergraduate or graduate studies at an accredited institution.
• Graduated from an accredited institution within the past 3 months; or
• Actively enrolled in a graduate program at an accredited institution.
• Must be 18 years or older at the time the appointment begins.
• Must possess a cumulative GPA of 3.0 on a 4.0 scale.
• Must be a U.S. citizen or Legal Permanent Resident at the time of application.
• The entirety of the appointment must be conducted within the United States.

Responsibilities

• Explore the log data streams to discover effective ways of tracking application failure rates.
• Write code to analyze, categorize, and visualize application interrupts.
• Help develop models of machine computational efficiency.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed