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Memory-Efficient Attention Kernels

Evaluate and integrate next-generation attention kernels to boost throughput while safeguarding reproducibility and reliability.

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Further reading and reference materials

  1. Attention kernel reverse-engineering reports (2025) – insights into optimization techniques and pitfalls.
  2. GPU profiling best practices (2024) – interpreting occupancy, warp efficiency, and memory throughput.
  3. Mixed precision training guides (2024–2025) – maintaining stability while leveraging FP8/BF16.
  4. Supply chain security frameworks for ML infrastructure (2025) – evaluating third-party kernel sources.
  5. Performance regression postmortems from large-scale model training (2024–2025) – lessons on monitoring and fallback strategies.
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