So, in the end, is `@fastmath` supposed to be adding FMA? Should I open an issue?
On Wednesday, September 21, 2016 at 7:11:14 PM UTC-7, Yichao Yu wrote: > > On Wed, Sep 21, 2016 at 9:49 PM, Erik Schnetter <schn...@gmail.com > <javascript:>> wrote: > > I confirm that I can't get Julia to synthesize a `vfmadd` instruction > > either... Sorry for sending you on a wild goose chase. > > -march=haswell does the trick for C (both clang and gcc) > the necessary bit for the machine ir optimization (this is not a llvm > ir optimization pass) to do this is llc options -mcpu=haswell and > function attribute unsafe-fp-math=true. > > > > > -erik > > > > On Wed, Sep 21, 2016 at 9:33 PM, Yichao Yu <yyc...@gmail.com > <javascript:>> wrote: > >> > >> On Wed, Sep 21, 2016 at 9:29 PM, Erik Schnetter <schn...@gmail.com > <javascript:>> > >> wrote: > >> > On Wed, Sep 21, 2016 at 9:22 PM, Chris Rackauckas <rack...@gmail.com > <javascript:>> > >> > 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. > >> > >> I've personally never seen llvm create fma from mul and add. We might > >> not have the llvm passes enabled if LLVM is capable of doing this at > >> all. > >> > >> > > >> > -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 <schn...@gmail.com <javascript:>> > >> > http://www.perimeterinstitute.ca/personal/eschnetter/ > > > > > > > > > > -- > > Erik Schnetter <schn...@gmail.com <javascript:>> > > http://www.perimeterinstitute.ca/personal/eschnetter/ >
