Computer Architecture Intern - 2027
NVIDIA China
What they're looking for
- Enhance GPU architecture to extend the state of the art in performance, efficiency, programmability and reliability
- Analyze, tune and prototype key applications for new GPU architectures
- Build model to predict performance, power and reliability on future architecture
- Design and develop tools to analyze, simulate, validate, and verify application performance and energy consumption
- Collaborate across the company to guide the direction of GPU computing, working with software, research and product team
The full posting
Published by NVIDIA. Original posting (opens in a new tab)
We are now looking for Computer Architecture Interns in our group!
The NVIDIA Architecture group is looking for world class architects and computer science interns to join and lead our various architecture efforts. A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for parallel processing algorithms. We are constantly looking for ways to improve our GPU architecture and maintain our leadership by developing new parallel programming models, new architectures and new infrastructure that is required to make this successful.
What you'll be doing:
• Enhance GPU architecture to extend the state of the art in performance, efficiency, programmability and reliability
• Analyze, tune and prototype key applications for new GPU architectures
• Build model to predict performance, power and reliability on future architecture
• Design and develop tools to analyze, simulate, validate, and verify application performance and energy consumption
• Collaborate across the company to guide the direction of GPU computing, working with software, research and product team
What we need to see:
• MS Degree in relevant discipline (CS, EE, Math). PhD helpful
• Expert programming skills in C or C++
• Strong background in computer architecture, parallel processing, signal processing and/or high performance computing
• Experience in characterizing and modeling system-level performance or power, executing comparison studies, and documenting and publishing results
Ways to stand out from the crowd:
• Optional/Plus -- Background in computer vision, machine learning, or any HPC domain
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