On Wed, Sep 21, 2016 at 9:22 PM, Chris Rackauckas <rackd...@gmail.com>
wrote:

> I'm not seeing `@fastmath` apply fma/muladd. I rebuilt the sysimg and now
> I get results where g and h apply muladd/fma in the native code, but a new
> function k which is `@fastmath` inside of f does not apply muladd/fma.
>
> https://gist.github.com/ChrisRackauckas/b239e33b4b52bcc28f3922c673a25910
>
> Should I open an issue?
>

In your case, LLVM apparently thinks that `x + x + 3` is faster to
calculate than `2x+3`. If you use a less round number than `2` multiplying
`x`, you might see a different behaviour.

-erik


Note that this is on v0.6 Windows. On Linux the sysimg isn't rebuilding for
> some reason, so I may need to just build from source.
>
> On Wednesday, September 21, 2016 at 6:22:06 AM UTC-7, Erik Schnetter wrote:
>>
>> On Wed, Sep 21, 2016 at 1:56 AM, Chris Rackauckas <rack...@gmail.com>
>> wrote:
>>
>>> Hi,
>>>   First of all, does LLVM essentially fma or muladd expressions like
>>> `a1*x1 + a2*x2 + a3*x3 + a4*x4`? Or is it required that one explicitly use
>>> `muladd` and `fma` on these types of instructions (is there a macro for
>>> making this easier)?
>>>
>>
>> Yes, LLVM will use fma machine instructions -- but only if they lead to
>> the same round-off error as using separate multiply and add instructions.
>> If you do not care about the details of conforming to the IEEE standard,
>> then you can use the `@fastmath` macro that enables several optimizations,
>> including this one. This is described in the manual <
>> http://docs.julialang.org/en/release-0.5/manual/performance
>> -tips/#performance-annotations>.
>>
>>
>>   Secondly, I am wondering if my setup is no applying these operations
>>> correctly. Here's my test code:
>>>
>>> f(x) = 2.0x + 3.0
>>> g(x) = muladd(x,2.0, 3.0)
>>> h(x) = fma(x,2.0, 3.0)
>>>
>>> @code_llvm f(4.0)
>>> @code_llvm g(4.0)
>>> @code_llvm h(4.0)
>>>
>>> @code_native f(4.0)
>>> @code_native g(4.0)
>>> @code_native h(4.0)
>>>
>>> *Computer 1*
>>>
>>> Julia Version 0.5.0-rc4+0
>>> Commit 9c76c3e* (2016-09-09 01:43 UTC)
>>> Platform Info:
>>>   System: Linux (x86_64-redhat-linux)
>>>   CPU: Intel(R) Xeon(R) CPU E5-2667 v4 @ 3.20GHz
>>>   WORD_SIZE: 64
>>>   BLAS: libopenblas (DYNAMIC_ARCH NO_AFFINITY Haswell)
>>>   LAPACK: libopenblasp.so.0
>>>   LIBM: libopenlibm
>>>   LLVM: libLLVM-3.7.1 (ORCJIT, broadwell)
>>>
>>
>> This looks good, the "broadwell" architecture that LLVM uses should imply
>> the respective optimizations. Try with `@fastmath`.
>>
>> -erik
>>
>>
>>
>>
>>
>>> (the COPR nightly on CentOS7) with
>>>
>>> [crackauc@crackauc2 ~]$ lscpu
>>> Architecture:          x86_64
>>> CPU op-mode(s):        32-bit, 64-bit
>>> Byte Order:            Little Endian
>>> CPU(s):                16
>>> On-line CPU(s) list:   0-15
>>> Thread(s) per core:    1
>>> Core(s) per socket:    8
>>> Socket(s):             2
>>> NUMA node(s):          2
>>> Vendor ID:             GenuineIntel
>>> CPU family:            6
>>> Model:                 79
>>> Model name:            Intel(R) Xeon(R) CPU E5-2667 v4 @ 3.20GHz
>>> Stepping:              1
>>> CPU MHz:               1200.000
>>> BogoMIPS:              6392.58
>>> Virtualization:        VT-x
>>> L1d cache:             32K
>>> L1i cache:             32K
>>> L2 cache:              256K
>>> L3 cache:              25600K
>>> NUMA node0 CPU(s):     0-7
>>> NUMA node1 CPU(s):     8-15
>>>
>>>
>>>
>>> I get the output
>>>
>>> define double @julia_f_72025(double) #0 {
>>> top:
>>>   %1 = fmul double %0, 2.000000e+00
>>>   %2 = fadd double %1, 3.000000e+00
>>>   ret double %2
>>> }
>>>
>>> define double @julia_g_72027(double) #0 {
>>> top:
>>>   %1 = call double @llvm.fmuladd.f64(double %0, double 2.000000e+00,
>>> double 3.000000e+00)
>>>   ret double %1
>>> }
>>>
>>> define double @julia_h_72029(double) #0 {
>>> top:
>>>   %1 = call double @llvm.fma.f64(double %0, double 2.000000e+00, double
>>> 3.000000e+00)
>>>   ret double %1
>>> }
>>> .text
>>> Filename: fmatest.jl
>>> pushq %rbp
>>> movq %rsp, %rbp
>>> Source line: 1
>>> addsd %xmm0, %xmm0
>>> movabsq $139916162906520, %rax  # imm = 0x7F40C5303998
>>> addsd (%rax), %xmm0
>>> popq %rbp
>>> retq
>>> nopl (%rax,%rax)
>>> .text
>>> Filename: fmatest.jl
>>> pushq %rbp
>>> movq %rsp, %rbp
>>> Source line: 2
>>> addsd %xmm0, %xmm0
>>> movabsq $139916162906648, %rax  # imm = 0x7F40C5303A18
>>> addsd (%rax), %xmm0
>>> popq %rbp
>>> retq
>>> nopl (%rax,%rax)
>>> .text
>>> Filename: fmatest.jl
>>> pushq %rbp
>>> movq %rsp, %rbp
>>> movabsq $139916162906776, %rax  # imm = 0x7F40C5303A98
>>> Source line: 3
>>> movsd (%rax), %xmm1           # xmm1 = mem[0],zero
>>> movabsq $139916162906784, %rax  # imm = 0x7F40C5303AA0
>>> movsd (%rax), %xmm2           # xmm2 = mem[0],zero
>>> movabsq $139925776008800, %rax  # imm = 0x7F43022C8660
>>> popq %rbp
>>> jmpq *%rax
>>> nopl (%rax)
>>>
>>> It looks like explicit muladd or not ends up at the same native code,
>>> but is that native code actually doing an fma? The fma native is different,
>>> but from a discussion on the Gitter it seems that might be a software FMA?
>>> This computer is setup with the BIOS setting as LAPACK optimized or
>>> something like that, so is that messing with something?
>>>
>>> *Computer 2*
>>>
>>> Julia Version 0.6.0-dev.557
>>> Commit c7a4897 (2016-09-08 17:50 UTC)
>>> Platform Info:
>>>   System: NT (x86_64-w64-mingw32)
>>>   CPU: Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz
>>>   WORD_SIZE: 64
>>>   BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)
>>>   LAPACK: libopenblas64_
>>>   LIBM: libopenlibm
>>>   LLVM: libLLVM-3.7.1 (ORCJIT, haswell)
>>>
>>>
>>> on a 4770k i7, Windows 10, I get the output
>>>
>>> ; Function Attrs: uwtable
>>> define double @julia_f_66153(double) #0 {
>>> top:
>>>   %1 = fmul double %0, 2.000000e+00
>>>   %2 = fadd double %1, 3.000000e+00
>>>   ret double %2
>>> }
>>>
>>> ; Function Attrs: uwtable
>>> define double @julia_g_66157(double) #0 {
>>> top:
>>>   %1 = call double @llvm.fmuladd.f64(double %0, double 2.000000e+00,
>>> double 3.000000e+00)
>>>   ret double %1
>>> }
>>>
>>> ; Function Attrs: uwtable
>>> define double @julia_h_66158(double) #0 {
>>> top:
>>>   %1 = call double @llvm.fma.f64(double %0, double 2.000000e+00, double
>>> 3.000000e+00)
>>>   ret double %1
>>> }
>>> .text
>>> Filename: console
>>> pushq %rbp
>>> movq %rsp, %rbp
>>> Source line: 1
>>> addsd %xmm0, %xmm0
>>> movabsq $534749456, %rax        # imm = 0x1FDFA110
>>> addsd (%rax), %xmm0
>>> popq %rbp
>>> retq
>>> nopl (%rax,%rax)
>>> .text
>>> Filename: console
>>> pushq %rbp
>>> movq %rsp, %rbp
>>> Source line: 2
>>> addsd %xmm0, %xmm0
>>> movabsq $534749584, %rax        # imm = 0x1FDFA190
>>> addsd (%rax), %xmm0
>>> popq %rbp
>>> retq
>>> nopl (%rax,%rax)
>>> .text
>>> Filename: console
>>> pushq %rbp
>>> movq %rsp, %rbp
>>> movabsq $534749712, %rax        # imm = 0x1FDFA210
>>> Source line: 3
>>> movsd dcabs164_(%rax), %xmm1  # xmm1 = mem[0],zero
>>> movabsq $534749720, %rax        # imm = 0x1FDFA218
>>> movsd (%rax), %xmm2           # xmm2 = mem[0],zero
>>> movabsq $fma, %rax
>>> popq %rbp
>>> jmpq *%rax
>>> nop
>>>
>>> This seems to be similar to the first result.
>>>
>>>
>>
>>
>> --
>> Erik Schnetter <schn...@gmail.com> http://www.perimeterinstitute.
>> ca/personal/eschnetter/
>>
>


-- 
Erik Schnetter <schnet...@gmail.com>
http://www.perimeterinstitute.ca/personal/eschnetter/

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