Research Aide - LCF - Qi, Kunting - 2.25.26.
About The Position
This research proposes to develop machine learning-based autotuning techniques for collective communication algorithm selection. Building on existing dynamic pipelining runtime infrastructure, the work will focus on creating performance models that can recommend suitable collective algorithms for common communication patterns and message sizes. The investigation will primarily center on offline autotuning through systematic benchmarking on representative HPC systems, generating training data to guide algorithm selection. The research will explore how historical performance profiles can inform collective operation decisions at runtime, with the goal of improving performance for typical scientific applications without requiring manual configuration by end users.
Requirements
- The entirety of the appointment must be conducted within the United States.
- Applicants must be: o Currently enrolled in undergraduate or graduate studies at an accredited institution. o Graduated from an accredited institution within the past 3 months; or o 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.
- If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies.
- Must complete a satisfactory background check.
Benefits
- comprehensive benefits are part of the total rewards package.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
No Education Listed
