As a global leader in artificial intelligence and graphics technology, theNVIDIA NVIDIA's interviews are known for being highly technical and industry-specific, and are especially demanding in areas such as algorithms, hardware architecture, and parallel computing. This article combines the recruitment characteristics of NVIDIA's engineer positions, and breaks down in detail NVIDIA Interview The process, core test points, and practical preparation suggestions are provided to help technical talents impact this AI era chip giant.
I. Coding
Implement matrix transposition using CUDA, require optimization of shared memory accesses to reduce Bank Conflict, and compare performance differences before and after optimization.
importation::
Input matrix dimension:N×N(assuming N is a multiple of 32, e.g., 512)
Storage format: Row-major
Core Examination Points: CUDA Thread Block and Grid Design; Bank Conflict Avoidance for Shared Memory; Asynchronous Data Transfer and Kernel Boot Optimization.
II. BQ
"Describe an experience where you optimized hardware-related algorithms in a project and how you balanced performance, power consumption, and code readability?"
Response framework (STAR rule)::
- SituationIn an autonomous driving project, we need to optimize the inference speed of the real-time target detection model on the on-board GPU (e.g., NVIDIA Jetson), and the original model has a delay of 200ms on the Jetson Nano, which is not able to meet the real-time requirement of 100ms.
- Task: Need to reduce latency by 50% without significantly increasing power consumption and code complexity.
- Action::
- Model quantization: using TensorRT to convert FP32 models to INT8 with a latency reduction of 30%.
- Layer Fusion Optimization: automatic layer fusion (Conv+BN+ReLU merge) via TensorRT with latency reduced by another 15%.
- Maintain code readability: encapsulate quantization and optimization logic as separate modules, keep FP32 branching for debugging, and add detailed comments.
- Result: The final latency is reduced to 90ms, power consumption is increased by only 5%, code structure is clear, and iteration cost is reduced.
III. System design questions
Design a target detection system supporting 100 real-time video streams, requiring end-to-end latency < 200ms and model accuracy ≥ mAP 0.75, based on NVIDIA GPU architecture (e.g., DGX A100 or Jetson series).
1. Hardware selection and architectural layering
edge node(Video Access Layer): The Jetson Xavier NX (6 TOPS) processes 20 720P video streams.
central server(Model Inference Layer): The DGX A100 (6240 TOPS) processes 80 1080P video streams in parallel over NVLink multi-card.
2. Software stack design
Video pre-processing: Asynchronous decoding and parallel scaling using the CUDA Video API.
cudaVideoCreateDecoder(&decoder, codec, nullptr); // Asynchronous decoding to GPU memory.
cudaVideoDecode(decoder, frame, stream); // Asynchronous decoding to GPU memory3. Performance optimization strategies
Model Compression: Reduce model parameters by 40% using NVIDIA TAO, keeping mAP ≥ 0.75.
Hardware Co-Optimization: Edge nodes are enabled with Jetson CVB to accelerate ROI trimming; center servers utilize A100 MIG to partition GPUs into separate instances.
4. Scalability and fault tolerance
Horizontal expansion: NVSwitch connects multiple DGX A100s for dynamic load balancing.
fault tolerance mechanism: Switching local caches when edge nodes are disconnected; server heartbeat detection and failover.
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