julia> versioninfo(true)
Julia Version 0.6.0-dev.1108
Commit 1efe487* (2016-10-24 13:47 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz
WORD_SIZE: 64
Ubuntu 14.04.5 LTS
uname: Linux 3.13.0-98-generic #145-Ubuntu SMP Sat Oct 8 20:13:07 UTC 2016 x86_64 x86_64
Memory: 62.8109245300293 GB (53626.91796875 MB free)
Uptime: 918243.0 sec
Load Avg: 0.130859375 1.2978515625 2.16796875
Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz:
speed user nice sys idle irq
#1-24 1200 MHz 1428837 s 12796 s 3318183 s
2198563210 s 3 s
BLAS: libmkl_rt
LAPACK: libmkl_rt
LIBM: libimf
LLVM: libLLVM-3.7.1 (ORCJIT, haswell)
I am consistently seeing OpenBLAS outperforming MKL by a non-trivial amount on peakflops(). I find this peculiar. Has anyone else encountered this?
_ _ _(_)_ | A fresh approach to technical computing
_ _ _| |_ __ _ | Type "?help" for help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 0.5.0 (2016-09-19 18:14 UTC)
|__/ | x86_64-pc-linux-gnu
julia> versioninfo()
Julia Version 0.5.0
Commit 3c9d753 (2016-09-19 18:14 UTC)
Platform Info:
System: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz
WORD_SIZE: 64
BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)
LAPACK: libopenblas64_
LIBM: libopenlibm
LLVM: libLLVM-3.7.1 (ORCJIT, haswell)
julia> [peakflops(10000) for i in 1:5]
5-element Array{Float64,1}:
2.85895e11
2.85431e11
2.85906e11
2.86397e11
2.85005e11
julia>
$ ./julia6
_
_ _ _(_)_ | A fresh approach to technical computing
_ _ _| |_ __ _ | Type "?help" for help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 0.6.0-dev.1108 (2016-10-24 13:47 UTC)
_/ |\__'_|_|_|\__'_| | Commit 1efe487* (0 days old master)
|__/ | x86_64-linux-gnu
julia> versioninfo()
Julia Version 0.6.0-dev.1108
Commit 1efe487* (2016-10-24 13:47 UTC)
DEBUG build
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz
WORD_SIZE: 64
BLAS: libmkl_rt
LAPACK: libmkl_rt
LIBM: libimf
LLVM: libLLVM-3.7.1 (ORCJIT, haswell)
julia> [peakflops(10000) for i in 1:5]
5-element Array{Float64,1}:
2.22788e11
2.37729e11
2.37163e11
2.35563e11
2.35813e11