Advanced CUDA
CUDA and Deep Learning
Deep learning frameworks hide much of CUDA complexity, but understanding what happens below the abstraction helps you debug bottlenecks, reduce cost, and build better custom operators.
Where CUDA fits in the stack
Frameworks like PyTorch and TensorFlow call into CUDA libraries such as cuBLAS, cuDNN, and NCCL. Your model code triggers kernels for matrix multiplication, convolution, normalization, and communication.
Practical optimization areas
- Mixed precision training with Tensor Cores.
- Efficient input pipelines to keep GPUs fed.
- Kernel fusion and custom CUDA extensions for hot paths.
- Memory-aware batching and gradient checkpointing.
Custom CUDA extensions
When default operators are too slow for a specialized workload, custom CUDA kernels can improve performance. The key is targeting true hotspots and validating numerical correctness.
Inference at scale
For production inference, latency and throughput trade-offs matter. CUDA stream management, batching policy, and memory reuse are often as important as model architecture.
Deep learning performance checklist
- Profile end-to-end time, not only kernel time.
- Track GPU utilization and host-side stalls.
- Use mixed precision where accuracy allows.
- Benchmark on realistic sequence lengths and batch sizes.