This intensive internship offers a unique opportunity to contribute to the development of a simulator and profiling framework for foundation model inference on NVidia GPUs.
Responsibilities include:
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Develop analytical performance models for GPU kernels and inference workloads.
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Build and validate a simulator to estimate theoretical hardware performance limits.
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Compare measured kernel performance against architectural peak throughput.
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Identify performance bottlenecks in compute, memory, communication, and scheduling.
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Analyze GPU execution using NVIDIA Nsight Systems and Nsight Compute.
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Investigate PTX and SASS code generation to understand low-level execution behavior.
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Collaborate with researchers and engineers to optimize inference kernels for transformer-based models.
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Evaluate utilization of Tensor Cores, memory bandwidth, caches, and instruction pipelines.
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Design profiling methodologies for Hopper and Blackwell architectures.
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Document findings and provide actionable recommendations for performance improvements.
Currently pursuing a degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, High-Performance Computing, or a related quantitative discipline.
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Experience with CUDA programming and GPU kernel development.
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Understanding of NVIDIA GPU architecture and memory hierarchy.
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Familiarity with performance profiling tools such as Nsight Systems and Nsight Compute.
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Knowledge of PTX, SASS, and low-level GPU execution.
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Experience optimizing CUDA kernels for throughput and latency.
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Understanding of roofline analysis, performance modeling, and hardware utilization metrics.
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Experience with deep learning frameworks such as PyTorch or TensorFlow.
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Strong programming skills in C++, CUDA, and Python.
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Performance engineering mindset.
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Strong analytical and debugging abilities.
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Interest in AI systems, inference optimization, and hardware-software co-design.
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Ability to work independently on research and engineering challenges.
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Excellent written and verbal communication skills.