Visiting Student – MCS – Xia, MIngze – 4.7.26

Argonne National Laboratory • Lemont, IL

3d

About The Position

Lossy compression has been employed to reduce the unprecedented amount of data produced by today’s large-scale scientific simulations and high-resolution instruments. However, generic lossy compressors may distort critical features in the data, leading to falsified discoveries. While feature-preserving compressors have been proposed to address this problem, they usually suffer from low throughput due to the additional computation to enable feature preservation. In this work, we will investigate how to enable high throughput of feature-preserving compressors leveraging GPUs, which requires both adaptation of the algorithm to massively parallel GPU architecture and tailored optimizations.

Requirements

• The entirety of the appointment must be conducted within the United States.
• Must be 18 years or older at the time the appointment begins.
• Applicants must be: 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

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed
• Number of Employees: 1,001-5,000 employees