GPU HBM vs SRAM bandwidth, online softmax tiling, IO-awareness, and Triton CUDA ops.
For a small example, four query vectors attend to four key vectors. Compute one block at a time, maintain online softmax statistics, and reproduce the same output without storing all 16 scores.
Standard attention runs out of memory as sequence length grows. Compare memory traffic, tile sizes, precision, masks, and output error while confirming mathematical equivalence.
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Predict how one control changes the FlashAttention & GPU Architecture result.
The starter deliberately follows a tempting but flawed assumption: Calling FlashAttention an approximation
Check: The browser checks pass and you can name the failure mechanism before opening the explanation.
Failing starter lab
Combine running max and weighted sums when a new tile arrives.
Run the incomplete starter before opening hints. Use the first wrong output or failed check to form a repair hypothesis.
Explanations after each answer · 80% to pass · FlashAttention & GPU Architecture
Chapter 1 of 6 · Core
Understanding modern CUDA performance requires analyzing GPU memory bandwidth bottlenecks.
Key equations
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