# \#avx

**URL:** https://discourse.julialang.org/tag/avx/880.md

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## [Failure to vectorize 8 Int64 multiplies when 8 Float64 multiplies vectorize](https://discourse.julialang.org/t/failure-to-vectorize-8-int64-multiplies-when-8-float64-multiplies-vectorize/114939)

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**Author:** [@brainandforce](https://discourse.julialang.org/u/brainandforce)\
**Replies:** 8\
**Last updated:** [May 31, 2024, 11:52pm UTC](https://discourse.julialang.org/t/failure-to-vectorize-8-int64-multiplies-when-8-float64-multiplies-vectorize/114939 "2024-05-31T23:52:39Z")

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I’ve come across this interesting observation in my package, CliffordNumbers.jl. For context, the multiplication done here is a geometric product (relevant code here) implemented as a grid multiply between blade coeffici…

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## [SC21: "Comparing Julia to Performance Portable Parallel Programming Models for HPC"](https://discourse.julialang.org/t/sc21-comparing-julia-to-performance-portable-parallel-programming-models-for-hpc/71686)

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**Author:** [@ImreSamu](https://discourse.julialang.org/u/ImreSamu)\
**Replies:** 2\
**Last updated:** [November 17, 2021, 10:01pm UTC](https://discourse.julialang.org/t/sc21-comparing-julia-to-performance-portable-parallel-programming-models-for-hpc/71686 "2021-11-17T22:01:36Z")

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SCI21 paper: Comparing Julia to Performance Portable Parallel Programming Models for HPC \[pdf\] Index Terms—Julia, OpenMP, OpenCL, Kokkos, CUDA, HIP, Performance Portability, Programming Models, GPUs context: Present…