llvm dev - Feb 2013 - [LLVMdev] [NVPTX] We need an LLVM CUDA math library, after all

Dear Yuan,

Sorry for delay with reply,

Answers on your questions could be different, depending on the math library
placement in the code generation pipeline. At KernelGen, we currently have
a user-level CUDA math module, adopted from cicc internals [1]. It is
intended to be linked with the user LLVM IR module, right before proceeding
with the final optimization and backend. Last few months we are using this
method to temporary workaround the absence of many math functions, to keep
up the speed of applications testing in our compiler test suite. Supplying
math in such way is not portable and introduces many issues, for instance:
1) The frontend (DragonEgg - in our case) must be taught to emit real math
functions calls instead those of LLVM intrinsics, NVPTX cannot handle
2) However, not all intrinsics should be replaced by math calls directly,
for example, there is not cdexp call, but it could be modelled with sincos.
3) Our math module assumes sm_20, and could be inefficient or non-portable
on other families of GPUs.

Instead of this approach, I think math library should be implemented *as a
lowering pass in backend*, working directly with intrinsics. In this case -
naming is not important, as well as final optimization is the job of
backend. But there is another important thing: backend should codegen math
with respect to accuracy settings, specified either as backend options, or
as functions attributes (quiet recent addition of LLVM). Accuracy settings
should be:
1) fast-math (ftz, prec-div, prec-sqrt, fma, etc.)
2) Use or not GPU-specific low-precision functions (__sin, __cos, etc.)

Following latter approach, math handling of NVPTX will conform the rest of
LLVM, and no host-dependant tweaks will be needed.

I'm also interested to contribute into this developments at reasonable
depth. Moving this part only on our own would slow down the progess with
main targets too much, that's why I'm asking for your help and
cooperation.

Best regards,
- Dima.

[1]
https://hpcforge.org/scm/viewvc.php/*checkout*/trunk/src/cuda/include/math.bc?root=kernelgen

2013/2/8 Yuan Lin <yulin at nvidia.com>
> Yes, it helps a lot and we are working on it.****
>
> ** **
>
> A few questions,****
>
> **1)      **What will be your use model of this library? Will you run
> optimization phases after linking with the library? If so, what are they?*
> ***
>
> **2)      **Do you care if the names of functions differ from those in
> libm? For example, it would be gpusin() instead of sin(). ****
>
> **3)      **Do you need a different library for different host platforms?
> Why?****
>
> **4)      **Any other functions (besides math) you want to see in this
> library?****
>
> ** **
>
> Thanks.****
>
> ** **
>
> Yuan****
>
> ** **
>
> ** **
>
> *From:* Dmitry Mikushin [mailto:dmitry at kernelgen.org]
> *Sent:* Thursday, February 07, 2013 2:09 PM
> *To:* Justin Holewinski; LLVM Developers Mailing List
> *Cc:* Yuan Lin
> *Subject:* [NVPTX] We need an LLVM CUDA math library, after all****
>
> ** **
>
> Hi Justin, gentlemen,
>
> I'm afraid I have to escalate this issue at this point. Since it was
> discussed for the first time last summer, it was sufficient for us for a
> while to have lowering of math calls into intrinsics disabled at DragonEgg
> level, and link them against CUDA math functions at LLVM IR level. Now I
> can say: this is not sufficient any longer, and we need NVPTX backend to
> deal with GPU math.
>
> > There also is no standard libm for PTX.
>
> Yes, that's right, but there is an interesting idea to codegen CUDA
math
> headers into LLVM IR and link it with user module at IR level. This method
> gives a perfect degree of flexibility with respect to high-level languages:
> the user no longer needs to deal with headers and can have math right in
> the IR, regardless the language it was lowered from. I can confirm this
> method works for us very well with C and Fortran, but in order to make
> accurate replacements of unsupported intrinsics calls, it needs to become
> aware of NVPTX backend capabilities in the form of:
>
> bool NVPTXTargetMachine::****
>
> isIntrinsicSupported(Function& intrinsic) and
> string NVPTXTargetMachine::whichMathCallReplacesIntrinsic(Function&
> intrinsic)
>
> > I would prefer not to lower such things in the back-end since
different
> compilers may want to implement such functions differently based on speed
> vs. accuracy trade-offs.
>
> Who are those different compilers? We are LLVM, the complete compiler
> stack, which should handle these things on its specific preference. Derived
> compilers may certainly think different, and it's their own business to
> change anything they want and never contribute back. We should not forget
> there are a lot of derived projects that use LLVM directly, like KernelGen
> or many of those embedded DSLs recently started flourishing. Their
> completeness and future relies on LLVM. For these reasons, I would strongly
> prefer LLVM/NVPTX should supply a reference GPU math implementation and
> invite you and everyone else to form a joint roadmap to deliver it.
>
> Before we started, IANAL, but something tells me there could be a
> licensing issue about releasing the LLVM IR emitted from CUDA headers.
> Could you please check this with NVIDIA?
>
> Many thanks,
> - D.
>
> 2012/9/6 Justin Holewinski <justin.holewinski at gmail.com>:
> > On 09/06/2012 10:02 AM, Dmitry N. Mikushin wrote:
> >>
> >> Dear all,
> >>
> >> During app compilation we have a crash in NVPTX backend:
> >>
> >> LLVM ERROR: Cannot select: 0x732b270: i64 =
ExternalSymbol'__powisf2'
> >> [ID=18]
> >>
> >> As I understand LLVM tries to lower the following call
> >>
> >> %28 = call ptx_device float @llvm.powi.f32(float 2.000000e+00, i32
%8)
> >> nounwind readonly
> >>
> >> to device intrinsic. The table llvm/IntrinsicsNVVM.td does not
contain
> >> such intrinsic, however it should be builtin, according to
> >> cuda/include/math_functions.h
> >
> >
> > It actually gets lowered into an external function call.
> >
> >
> >>
> >> Is my understanding correct, and we need simply add the
corresponding
> >> definition to llvm/IntrinsicsNVVM.td ? How to do that, what are
the
> >> rules?
> >
> >
> > PTX does not have an instruction (or simple series of instructions)
that
> > implements pow, so this will not be handled.  I would prefer not to
lower
> > such things in the back-end since different compilers may want to
> implement
> > such functions differently based on speed vs. accuracy trade-offs.
> >
> > There also is no standard libm for PTX.  It is up to the higher-level
> > compiler to link against a run-time library that provides functions
like
> pow
> > (see include/math_functions.h in a CUDA distribution).
> >
> >>
> >> Thanks,
> >> - D.
> >> _______________________________________________
> >> LLVM Developers mailing list
> >> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
> >> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
> >****
>
> ****
>
> >
> > --
> > Thanks,
> >
> > Justin Holewinski
> >****
>  ------------------------------
>  This email message is for the sole use of the intended recipient(s) and
> may contain confidential information.  Any unauthorized review, use,
> disclosure or distribution is prohibited.  If you are not the intended
> recipient, please contact the sender by reply email and destroy all copies
> of the original message.
>  ------------------------------
>
>
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I would be very hesitant to expose all math library functions as
intrinsics.  I believe linking with a target-specific math library is the
correct approach, as it decouples the back end from the needs of the source
program/language.  Users should be free to use any math library
implementation they choose.  Intrinsics are meant for functions that
compile down to specific isa features, like fused multiply add and square
root.
On Feb 16, 2013 8:46 PM, "Dmitry Mikushin" <dmitry at
kernelgen.org> wrote:
> Dear Yuan,
>
> Sorry for delay with reply,
>
> Answers on your questions could be different, depending on the math
> library placement in the code generation pipeline. At KernelGen, we
> currently have a user-level CUDA math module, adopted from cicc internals
> [1]. It is intended to be linked with the user LLVM IR module, right before
> proceeding with the final optimization and backend. Last few months we are
> using this method to temporary workaround the absence of many math
> functions, to keep up the speed of applications testing in our compiler
> test suite. Supplying math in such way is not portable and introduces many
> issues, for instance:
> 1) The frontend (DragonEgg - in our case) must be taught to emit real math
> functions calls instead those of LLVM intrinsics, NVPTX cannot handle
> 2) However, not all intrinsics should be replaced by math calls directly,
> for example, there is not cdexp call, but it could be modelled with sincos.
> 3) Our math module assumes sm_20, and could be inefficient or non-portable
> on other families of GPUs.
>
> Instead of this approach, I think math library should be implemented *as
> a lowering pass in backend*, working directly with intrinsics. In this
> case - naming is not important, as well as final optimization is the job of
> backend. But there is another important thing: backend should codegen math
> with respect to accuracy settings, specified either as backend options, or
> as functions attributes (quiet recent addition of LLVM). Accuracy settings
> should be:
> 1) fast-math (ftz, prec-div, prec-sqrt, fma, etc.)
> 2) Use or not GPU-specific low-precision functions (__sin, __cos, etc.)
>
> Following latter approach, math handling of NVPTX will conform the rest of
> LLVM, and no host-dependant tweaks will be needed.
>
> I'm also interested to contribute into this developments at reasonable
> depth. Moving this part only on our own would slow down the progess with
> main targets too much, that's why I'm asking for your help and
cooperation.
>
> Best regards,
> - Dima.
>
> [1]
>
https://hpcforge.org/scm/viewvc.php/*checkout*/trunk/src/cuda/include/math.bc?root=kernelgen
>
> 2013/2/8 Yuan Lin <yulin at nvidia.com>
>
>> Yes, it helps a lot and we are working on it.****
>>
>> ** **
>>
>> A few questions,****
>>
>> **1)      **What will be your use model of this library? Will you run
>> optimization phases after linking with the library? If so, what are
they?
>> ****
>>
>> **2)      **Do you care if the names of functions differ from those in
>> libm? For example, it would be gpusin() instead of sin(). ****
>>
>> **3)      **Do you need a different library for different host
>> platforms? Why?****
>>
>> **4)      **Any other functions (besides math) you want to see in this
>> library?****
>>
>> ** **
>>
>> Thanks.****
>>
>> ** **
>>
>> Yuan****
>>
>> ** **
>>
>> ** **
>>
>> *From:* Dmitry Mikushin [mailto:dmitry at kernelgen.org]
>> *Sent:* Thursday, February 07, 2013 2:09 PM
>> *To:* Justin Holewinski; LLVM Developers Mailing List
>> *Cc:* Yuan Lin
>> *Subject:* [NVPTX] We need an LLVM CUDA math library, after all****
>>
>> ** **
>>
>> Hi Justin, gentlemen,
>>
>> I'm afraid I have to escalate this issue at this point. Since it
was
>> discussed for the first time last summer, it was sufficient for us for
a
>> while to have lowering of math calls into intrinsics disabled at
DragonEgg
>> level, and link them against CUDA math functions at LLVM IR level. Now
I
>> can say: this is not sufficient any longer, and we need NVPTX backend
to
>> deal with GPU math.
>>
>> > There also is no standard libm for PTX.
>>
>> Yes, that's right, but there is an interesting idea to codegen CUDA
math
>> headers into LLVM IR and link it with user module at IR level. This
method
>> gives a perfect degree of flexibility with respect to high-level
languages:
>> the user no longer needs to deal with headers and can have math right
in
>> the IR, regardless the language it was lowered from. I can confirm this
>> method works for us very well with C and Fortran, but in order to make
>> accurate replacements of unsupported intrinsics calls, it needs to
become
>> aware of NVPTX backend capabilities in the form of:
>>
>> bool NVPTXTargetMachine::****
>>
>> isIntrinsicSupported(Function& intrinsic) and
>> string NVPTXTargetMachine::whichMathCallReplacesIntrinsic(Function&
>> intrinsic)
>>
>> > I would prefer not to lower such things in the back-end since
different
>> compilers may want to implement such functions differently based on
speed
>> vs. accuracy trade-offs.
>>
>> Who are those different compilers? We are LLVM, the complete compiler
>> stack, which should handle these things on its specific preference.
Derived
>> compilers may certainly think different, and it's their own
business to
>> change anything they want and never contribute back. We should not
forget
>> there are a lot of derived projects that use LLVM directly, like
KernelGen
>> or many of those embedded DSLs recently started flourishing. Their
>> completeness and future relies on LLVM. For these reasons, I would
strongly
>> prefer LLVM/NVPTX should supply a reference GPU math implementation and
>> invite you and everyone else to form a joint roadmap to deliver it.
>>
>> Before we started, IANAL, but something tells me there could be a
>> licensing issue about releasing the LLVM IR emitted from CUDA headers.
>> Could you please check this with NVIDIA?
>>
>> Many thanks,
>> - D.
>>
>> 2012/9/6 Justin Holewinski <justin.holewinski at gmail.com>:
>> > On 09/06/2012 10:02 AM, Dmitry N. Mikushin wrote:
>> >>
>> >> Dear all,
>> >>
>> >> During app compilation we have a crash in NVPTX backend:
>> >>
>> >> LLVM ERROR: Cannot select: 0x732b270: i64 =
ExternalSymbol'__powisf2'
>> >> [ID=18]
>> >>
>> >> As I understand LLVM tries to lower the following call
>> >>
>> >> %28 = call ptx_device float @llvm.powi.f32(float 2.000000e+00,
i32 %8)
>> >> nounwind readonly
>> >>
>> >> to device intrinsic. The table llvm/IntrinsicsNVVM.td does not
contain
>> >> such intrinsic, however it should be builtin, according to
>> >> cuda/include/math_functions.h
>> >
>> >
>> > It actually gets lowered into an external function call.
>> >
>> >
>> >>
>> >> Is my understanding correct, and we need simply add the
corresponding
>> >> definition to llvm/IntrinsicsNVVM.td ? How to do that, what
are the
>> >> rules?
>> >
>> >
>> > PTX does not have an instruction (or simple series of
instructions) that
>> > implements pow, so this will not be handled.  I would prefer not
to
>> lower
>> > such things in the back-end since different compilers may want to
>> implement
>> > such functions differently based on speed vs. accuracy trade-offs.
>> >
>> > There also is no standard libm for PTX.  It is up to the
higher-level
>> > compiler to link against a run-time library that provides
functions
>> like pow
>> > (see include/math_functions.h in a CUDA distribution).
>> >
>> >>
>> >> Thanks,
>> >> - D.
>> >> _______________________________________________
>> >> LLVM Developers mailing list
>> >> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
>> >> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
>> >****
>>
>> ****
>>
>> >
>> > --
>> > Thanks,
>> >
>> > Justin Holewinski
>> >****
>>  ------------------------------
>>  This email message is for the sole use of the intended recipient(s)
and
>> may contain confidential information.  Any unauthorized review, use,
>> disclosure or distribution is prohibited.  If you are not the intended
>> recipient, please contact the sender by reply email and destroy all
copies
>> of the original message.
>>  ------------------------------
>>
>>
>
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Hi Justin,

I don't understand, why, for instance, X86 backend handles pow
automatically, and NVPTX should be a PITA requiring user to bring his own
pow implementation. Even at a very general level, this limits the interest
of users to LLVM NVPTX backend. Could you please elaborate on the rationale
behind your point? Why the accuracy modes I suggested are not sufficient,
in your opinion?

- D.

2013/2/17 Justin Holewinski <justin.holewinski at gmail.com>
> I would be very hesitant to expose all math library functions as
> intrinsics.  I believe linking with a target-specific math library is the
> correct approach, as it decouples the back end from the needs of the source
> program/language.  Users should be free to use any math library
> implementation they choose.  Intrinsics are meant for functions that
> compile down to specific isa features, like fused multiply add and square
> root.
> On Feb 16, 2013 8:46 PM, "Dmitry Mikushin" <dmitry at
kernelgen.org> wrote:
>
>> Dear Yuan,
>>
>> Sorry for delay with reply,
>>
>> Answers on your questions could be different, depending on the math
>> library placement in the code generation pipeline. At KernelGen, we
>> currently have a user-level CUDA math module, adopted from cicc
internals
>> [1]. It is intended to be linked with the user LLVM IR module, right
before
>> proceeding with the final optimization and backend. Last few months we
are
>> using this method to temporary workaround the absence of many math
>> functions, to keep up the speed of applications testing in our compiler
>> test suite. Supplying math in such way is not portable and introduces
many
>> issues, for instance:
>> 1) The frontend (DragonEgg - in our case) must be taught to emit real
>> math functions calls instead those of LLVM intrinsics, NVPTX cannot
handle
>> 2) However, not all intrinsics should be replaced by math calls
directly,
>> for example, there is not cdexp call, but it could be modelled with
sincos.
>> 3) Our math module assumes sm_20, and could be inefficient or
>> non-portable on other families of GPUs.
>>
>> Instead of this approach, I think math library should be implemented
*as
>> a lowering pass in backend*, working directly with intrinsics. In this
>> case - naming is not important, as well as final optimization is the
job of
>> backend. But there is another important thing: backend should codegen
math
>> with respect to accuracy settings, specified either as backend options,
or
>> as functions attributes (quiet recent addition of LLVM). Accuracy
settings
>> should be:
>> 1) fast-math (ftz, prec-div, prec-sqrt, fma, etc.)
>> 2) Use or not GPU-specific low-precision functions (__sin, __cos, etc.)
>>
>> Following latter approach, math handling of NVPTX will conform the rest
>> of LLVM, and no host-dependant tweaks will be needed.
>>
>> I'm also interested to contribute into this developments at
reasonable
>> depth. Moving this part only on our own would slow down the progess
with
>> main targets too much, that's why I'm asking for your help and
cooperation.
>>
>> Best regards,
>> - Dima.
>>
>> [1]
>>
https://hpcforge.org/scm/viewvc.php/*checkout*/trunk/src/cuda/include/math.bc?root=kernelgen
>>
>> 2013/2/8 Yuan Lin <yulin at nvidia.com>
>>
>>> Yes, it helps a lot and we are working on it.****
>>>
>>> ** **
>>>
>>> A few questions,****
>>>
>>> **1)      **What will be your use model of this library? Will you
run
>>> optimization phases after linking with the library? If so, what are
they?
>>> ****
>>>
>>> **2)      **Do you care if the names of functions differ from those
in
>>> libm? For example, it would be gpusin() instead of sin(). ****
>>>
>>> **3)      **Do you need a different library for different host
>>> platforms? Why?****
>>>
>>> **4)      **Any other functions (besides math) you want to see in
this
>>> library?****
>>>
>>> ** **
>>>
>>> Thanks.****
>>>
>>> ** **
>>>
>>> Yuan****
>>>
>>> ** **
>>>
>>> ** **
>>>
>>> *From:* Dmitry Mikushin [mailto:dmitry at kernelgen.org]
>>> *Sent:* Thursday, February 07, 2013 2:09 PM
>>> *To:* Justin Holewinski; LLVM Developers Mailing List
>>> *Cc:* Yuan Lin
>>> *Subject:* [NVPTX] We need an LLVM CUDA math library, after all****
>>>
>>> ** **
>>>
>>> Hi Justin, gentlemen,
>>>
>>> I'm afraid I have to escalate this issue at this point. Since
it was
>>> discussed for the first time last summer, it was sufficient for us
for a
>>> while to have lowering of math calls into intrinsics disabled at
DragonEgg
>>> level, and link them against CUDA math functions at LLVM IR level.
Now I
>>> can say: this is not sufficient any longer, and we need NVPTX
backend to
>>> deal with GPU math.
>>>
>>> > There also is no standard libm for PTX.
>>>
>>> Yes, that's right, but there is an interesting idea to codegen
CUDA math
>>> headers into LLVM IR and link it with user module at IR level. This
method
>>> gives a perfect degree of flexibility with respect to high-level
languages:
>>> the user no longer needs to deal with headers and can have math
right in
>>> the IR, regardless the language it was lowered from. I can confirm
this
>>> method works for us very well with C and Fortran, but in order to
make
>>> accurate replacements of unsupported intrinsics calls, it needs to
become
>>> aware of NVPTX backend capabilities in the form of:
>>>
>>> bool NVPTXTargetMachine::****
>>>
>>> isIntrinsicSupported(Function& intrinsic) and
>>> string
NVPTXTargetMachine::whichMathCallReplacesIntrinsic(Function&
>>> intrinsic)
>>>
>>> > I would prefer not to lower such things in the back-end since
>>> different compilers may want to implement such functions
differently based
>>> on speed vs. accuracy trade-offs.
>>>
>>> Who are those different compilers? We are LLVM, the complete
compiler
>>> stack, which should handle these things on its specific preference.
Derived
>>> compilers may certainly think different, and it's their own
business to
>>> change anything they want and never contribute back. We should not
forget
>>> there are a lot of derived projects that use LLVM directly, like
KernelGen
>>> or many of those embedded DSLs recently started flourishing. Their
>>> completeness and future relies on LLVM. For these reasons, I would
strongly
>>> prefer LLVM/NVPTX should supply a reference GPU math implementation
and
>>> invite you and everyone else to form a joint roadmap to deliver it.
>>>
>>> Before we started, IANAL, but something tells me there could be a
>>> licensing issue about releasing the LLVM IR emitted from CUDA
headers.
>>> Could you please check this with NVIDIA?
>>>
>>> Many thanks,
>>> - D.
>>>
>>> 2012/9/6 Justin Holewinski <justin.holewinski at gmail.com>:
>>> > On 09/06/2012 10:02 AM, Dmitry N. Mikushin wrote:
>>> >>
>>> >> Dear all,
>>> >>
>>> >> During app compilation we have a crash in NVPTX backend:
>>> >>
>>> >> LLVM ERROR: Cannot select: 0x732b270: i64 =
ExternalSymbol'__powisf2'
>>> >> [ID=18]
>>> >>
>>> >> As I understand LLVM tries to lower the following call
>>> >>
>>> >> %28 = call ptx_device float @llvm.powi.f32(float
2.000000e+00, i32 %8)
>>> >> nounwind readonly
>>> >>
>>> >> to device intrinsic. The table llvm/IntrinsicsNVVM.td does
not contain
>>> >> such intrinsic, however it should be builtin, according to
>>> >> cuda/include/math_functions.h
>>> >
>>> >
>>> > It actually gets lowered into an external function call.
>>> >
>>> >
>>> >>
>>> >> Is my understanding correct, and we need simply add the
corresponding
>>> >> definition to llvm/IntrinsicsNVVM.td ? How to do that,
what are the
>>> >> rules?
>>> >
>>> >
>>> > PTX does not have an instruction (or simple series of
instructions)
>>> that
>>> > implements pow, so this will not be handled.  I would prefer
not to
>>> lower
>>> > such things in the back-end since different compilers may want
to
>>> implement
>>> > such functions differently based on speed vs. accuracy
trade-offs.
>>> >
>>> > There also is no standard libm for PTX.  It is up to the
higher-level
>>> > compiler to link against a run-time library that provides
functions
>>> like pow
>>> > (see include/math_functions.h in a CUDA distribution).
>>> >
>>> >>
>>> >> Thanks,
>>> >> - D.
>>> >> _______________________________________________
>>> >> LLVM Developers mailing list
>>> >> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
>>> >> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
>>> >****
>>>
>>> ****
>>>
>>> >
>>> > --
>>> > Thanks,
>>> >
>>> > Justin Holewinski
>>> >****
>>>  ------------------------------
>>>  This email message is for the sole use of the intended
recipient(s)
>>> and may contain confidential information.  Any unauthorized review,
use,
>>> disclosure or distribution is prohibited.  If you are not the
intended
>>> recipient, please contact the sender by reply email and destroy all
copies
>>> of the original message.
>>>  ------------------------------
>>>
>>>
>>
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