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Loop Analysis and Vectorization in Julia - JuliaCon 2020
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- 1 Welcome!
- 2 Loop vectorization and LoopVectorization.jl
- 3 Current limitations of LoopVectorization.jl
- 4 First main part of intra-core parallelism: Single Instruction Multiple Data (SIMD)
- 5 Loading and storing vectors
- 6 Second main part of intra-core parallelism: super scalar parallelism
- 7 Example: summing a vector
- 8 Problem: not all vectors have a length that is multiple of 32
- 9 Vectorization of the loop with @avx
- 10 @avx and functions like log from stdlib
- 11 @avx and StructArrays.jl
- 12 Eliminating redundant operations
- 13 LoopVectorization.jl and generated functions
- 14 Redundancy in convolutions
- 15 Internal working of LoopVectorization.jl
