Comments (5)
Just for reference, Wikipedia has a pretty good list of specs for FX processors: https://en.m.wikipedia.org/wiki/List_of_AMD_FX_microprocessors
from loopvectorization.jl.
I no longer call how I decided not to use:
using CpuId
cache_size = CpuId.cachesize()
num_cores = CpuId.cpucores()
https://github.com/chriselrod/VectorizationBase.jl/blob/master/deps/build.jl#L10
For AMD CPUS. Mind trying them and reporting what they return?
It would be better if we don't have to special case.
If that doesn't work, the problem is in CpuId.jl
. If it does work, I should probably drop the checks.
Either way, the issue can be addressed in VectorizationBase, like @simeonschaub noted on discourse.
Otherwise, I'll add them. I also need to add Sys.CPU_NAME == "znver2"
.
What is your Sys.CPU_NAME
?
from loopvectorization.jl.
julia> cache_size = CpuId.cachesize()
(16384, 2097152, 8388608)
julia> num_cores = CpuId.cpucores()
8
julia> Sys.CPU_NAME
"bdver1"
from loopvectorization.jl.
Looks like I may be able to just drop the special casing for AMD.
Do you know how I could test on zenv1 and zenv2?
I'm wondering if I should still special case these, or if they're properly supported. It isn't immediately obvious to me how to find out without access to them.
EDIT:
m-j-w/CpuId.jl@2ac67b0
Looks like as of July 2018, those functions should be supported on AMD.
I must have written the build script before that.
I'll just drop the special casing.
from loopvectorization.jl.
Fixed by
JuliaSIMD/VectorizationBase.jl@720d707
Update to VectorizationBase v0.1.8. I'll release a new LoopVectorization with that as the lower bound in the next few days.
from loopvectorization.jl.
Related Issues (20)
- `vtrunc(::Float64)` issue HOT 3
- Strange compile behavior for @turbo HOT 2
- is it possible to set @turbo thread = true/false at runtime? HOT 3
- LoopVectorization fail to compile on julia 32bit REPL
- AssertionError: M == 1 HOT 9
- Inconsistent results w/ and w/o @turbo HOT 6
- vfilter with multiple conditions HOT 2
- Memory corruption HOT 2
- Incorrect results using @turbo with linear array indexing HOT 1
- Weird/inconsistent behavior with constant lhs indexing inside @turbo loop HOT 2
- Suboptimal Choice of the Vecotrization Level for Image Convolution HOT 1
- Performance for stride 2
- Bad IR generation triggers assertion failure on 1.11
- Release v0.12.167 breaks RecursiveFactorization on Julia v1.11+ HOT 12
- LoopVectorization.jl causing segfaults on 1.11 HOT 5
- type inference issue with vectors if ints and floats in julia 1.10 HOT 3
- LoadError: BoundsError: attempt to access 2-element Vector{LoopVectorization.ArrayReferenceMeta} at index [0] HOT 1
- Reduction not found HOT 2
- Safety of generating random numbers HOT 2
- Problem/error in execution order
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from loopvectorization.jl.