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Advanced•MLOps
FlashAttention & GPU Architecture
GPU HBM vs SRAM bandwidth, online softmax tiling, IO-awareness, and Triton CUDA ops.
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Research-Level Deep Dive & Equations
Understanding modern CUDA performance requires analyzing GPU memory bandwidth bottlenecks.
•GPU Memory Hierarchy Specs (NVIDIA A100 80GB Tensor Core GPU):
- **High Bandwidth Memory (HBM2e / DRAM)**: Capacity , Bandwidth .
- **On-Chip SRAM (L1 Cache / Shared Memory)**: Capacity per GPU, Bandwidth ( faster!).
•Memory-Bound vs Compute-Bound Operations:
- **Compute-Bound (MatMul)**: High arithmetic intensity (FLOPs / Byte). Tensor Cores operate at peak (FP16).
- **Memory-Bound (Softmax, LayerNorm, Elementwise)**: Low arithmetic intensity. The GPU spends of execution time waiting for data to travel across the slow HBM bus!
Key Equations
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