Loop Analysis and Vectorization in Julia - JuliaCon 2020

Loop Analysis and Vectorization in Julia - JuliaCon 2020

The Julia Programming Language via YouTube Direct link

Example: summing a vector

7 of 15

7 of 15

Example: summing a vector

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Loop Analysis and Vectorization in Julia - JuliaCon 2020

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  1. 1 Welcome!
  2. 2 Loop vectorization and LoopVectorization.jl
  3. 3 Current limitations of LoopVectorization.jl
  4. 4 First main part of intra-core parallelism: Single Instruction Multiple Data (SIMD)
  5. 5 Loading and storing vectors
  6. 6 Second main part of intra-core parallelism: super scalar parallelism
  7. 7 Example: summing a vector
  8. 8 Problem: not all vectors have a length that is multiple of 32
  9. 9 Vectorization of the loop with @avx
  10. 10 @avx and functions like log from stdlib
  11. 11 @avx and StructArrays.jl
  12. 12 Eliminating redundant operations
  13. 13 LoopVectorization.jl and generated functions
  14. 14 Redundancy in convolutions
  15. 15 Internal working of LoopVectorization.jl

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