~/talk

Coarsening Optimization for Differentiable Programming

on YouTube 15m
Talkat OOPSLA 2021 · 2021-10-17..10-22· [machine-learning, compilers-and-language-design]

speakers

name:Xipeng Shen
links:none on file
name:Guoqiang Zhang
links:none on file
name:Irene Dea
links:none on file
name:Samantha Andow
links:none on file
name:Emilio Arroyo-Fang
links:none on file
name:Neal Gafter
links:none on file
name:Johann George
links:none on file
name:Melissa Grueter
links:none on file
name:Erik Meijer
x:headinthebox
name:Olin Shivers
links:none on file
name:Steffi Stumpos
links:none on file
name:Alanna Tempest
links:none on file
name:Christy Warden
links:none on file
name:Shannon Yang
links:none on file

resources

video:https://youtu.be/Xwy7s4u2Jn4
slides:none on file

summary

Paper presents coarsening optimization that raises segments to symbolic level, applies differentiation and lowers optimized code, and reports several-times to over 100× speedups on real-world apps. It builds a compiler pass that combines algorithmic and symbolic AD, introduces phi-calculus to symbolically differentiate loops and branches without expression swell, and adds reuse-centric SOI identification to balance reuse versus coarsening. Implementation generates lowered source and empirical results show much lower allocations and large runtime reductions.

AI-written summary · about

chapters (14)

  1. 0:00Introduction
  2. 0:15What is Differentiable Programming
  3. 2:30Algorithmic Differentiation
  4. 3:28Symbolic Differentiation
  5. 5:13Challenges
  6. 5:24Control flows
  7. 5:46Fair calculus
  8. 6:06SSA
  9. 7:25Loops
  10. 8:07Formulas
  11. 10:55Loop Example
  12. 11:46ReuseAware SOI Identification
  13. 12:21Benchmarks
  14. 14:12Conclusion

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