R devel - Apr 2022 - Matrix issues when building R with znver3 architecture under GCC 11

Hi Kieran,

On 4/12/22 02:36, Kieran Short wrote:
> Hello,
>
> I'm new to this list, and have subscribed particularly because I've
come
> across an issue with building R from source with an AMD-based Zen
> architecture under GCC11. Please don't attack me for my linux operating
> system choice, but it is Ubuntu 20.04 with Linux Kernel 5.10.102.1 -
> microsoft-standard-WSL2. I've built GCC11 using GCC8 (the standard GCC
> under Ubuntu20.04 WSL release), under Windows11 with wslg. WSL2/g runs as a
> hypervisor with ports to all system resources including display, GPU (cuda,
> etc).
>
> The reason why I am posting this email is that I am trying to compile R
> using the AMD Zen3 platform architecture rather than x86/64, because it has
> processor-specific optimizations that improve performance over the standard
> x86/64 in benchmarks. The Zen3 architecture optimizations are not available
> in earlier versions of GCC (actually, they have possibly been backported to
> GCC10 now). Since Ubuntu 20.04 doesn't have GCC11, I compiled the GCC11
> compiler using the native GCC8.
>
> The GCC11 I have built can build R 4.1.3 with a standard x86-64
> architecture and pass all tests with "make check-all".
> I configured that with:
>> ~/R/R-4.1.3/configure CC=gcc-11.2 CXX=g++-11.2 FC=gfortran-11.2
> CXXFLAGS="-O3 -march=x86-64" CFLAGS="-O3 -march=x86-64"
FFLAGS="-O3
> -march=x86-64" --enable-memory-profiling --enable-R-shlib
> and built with
>> make -j 32 -O
>> make check-all
> ## PASS.
>
> So I can build R in my environment with GCC11.
> In configure, I am using references to "gcc-11.2"
"gfortran-11.2" and
> "g++-11.2" because I compiled GCC11 compilers with these
suffixes.
>
> Now, I'm using a 32 thread (16 core) AMD Zen3 CPU (a 5950x), and want
to
> use it to its full potential. Zen3 optimizations are available as a
> -march=znver3 option n GCC11. The znver3 optimizations improve performance
> in Phoronix Test Suite benchmarks (I'm not aware of anyone that has
> compiled R with them). See:
> https://www.phoronix.com/scan.php?page=article&item=amd-5950x-gcc11
>
> However, the R 4.1.3 build (made with "make -j 32 -O"),
configured with
> -march=znver3, produces an R that fails "make check-all".
>
>> ~/R/R-4.1.3/configure CC=gcc-11.2 CXX=g++-11.2 FC=gfortran-11.2
> CXXFLAGS="-O2 -march=znver3" CFLAGS="-O2 -march=znver3"
FFLAGS="-O2
> -march=znver3" --enable-memory-profiling --enable-R-shlib
> or
>> ~/R/R-4.1.3/configure CC=gcc-11.2 CXX=g++-11.2 FC=gfortran-11.2
> CXXFLAGS="-O3 -march=znver3" CFLAGS="-O3 -march=znver3"
FFLAGS="-O3
> -march=znver3" --enable-memory-profiling --enable-R-shlib
>
> The fail is always in the factorizing.R Matrix.R tests, and in particular,
> there are a number of errors and a fatal error.
> I have attached the output because I cannot really understand what is going
> wrong. But results returned from matrix calculations are obviously odd with
> -march=znver3 in GCC 11. There is another backwards-compatible architecture
> option "znver2" and this has EXACTLY the same result.
>
> While there are other warrnings and errors (many in assert.EQ() ), the
> factorizing.R script continues. The fatal error (at line 2662 in the
> attached factorizing.Rout.fail text file) is:
>
>> ## problematic rank deficient rankMatrix() case -- only seen in large
> cases ??
>> Z. <- readRDS(system.file("external",
"Z_NA_rnk.rds", package="Matrix"))
>> tools::assertWarning(rnkZ. <- rankMatrix(Z., method =
"qr")) # gave errors
> Error in assertCondition(expr, classes, .exprString = d.expr) :
>    Failed to get warning in evaluating rnkZ. <- rankMatrix(Z., method 
...
> Calls: <Anonymous> -> assertCondition
> Execution halted
>
> Can anybody shed light on what might be going on here? 'make
check-all'
> passes all the other checks. It is just factorizing.R in Matrix that fails
> (other matrix tests run ok).
> Sorry this is a bit long-winded, but I thought details might be important.
R gets used and tested most with the default optimizations, without use 
of model-specific instructions and with -O2 (GCC). It happens time to 
time that some people try other optimization options and run into 
problems. In principle, there are these cases (seen before):

(1) the test in R package (or R) is wrong - it (unintentionally) expects 
behavior which has been observed in builds with default optimizations, 
but is not necessarily the only correct one; in case of numerical 
tolerances set empirically, they could simply be too tight

(2) the algorithm in R package or R has a bug - the result is really 
wrong and it is because the algorithm is (unintentionally) not portable 
enough, it (unintentionally) only works with default optimizations or 
lower; in case of numerical results, this can be because it expects more 
precision from the floating point computations than mandated by IEEE, or 
assumes behavior not mandated

(3) the optimization by design violates some properties the algorithm 
knowingly depends on; with numerical computations, this can be a sort of 
"fast" (and similarly referred to) mode which violates IEEE floating 
point standard by design, in the aim of better performance; due to the 
nature of the algorithm depending on IEEE, and poor luck, the results 
end up completely wrong

(4) there is a bug in the C or Fortran compiler (GCC as we use GCC) that 
only exhibits with the unusual optimizations; the compiler produces 
wrong code

So, when you run into a problem like this and want to get that fixed, 
the first thing is to identify which case of the above it is, in case of 
1 and 2 also differentiate between base R and a package (and which 
concrete package). Different people maintain these things and you would 
ideally narrow down the problem to a very small, isolated, reproducible 
example to support your claim where the bug is. If you do this right, 
the problem can often get fixed very fast.

Such an example for (1) could be: few lines of standalone R code using 
Matrix that produces correct results, but the test is not happy. With 
pointers to the real check in the tests that is wrong. And an 
explanation why the result is wrong.

For (2)-(4) it would be a minimal standalone C/Fortran example including 
only the critical function/part of algorithm that is not correct/not 
portable/not compiled correctly, with results obtained with 
optimizations where it works and where it doesn't. Unless you find an 
obvious bug in R easy to explain (2), when the example would not have to 
be standalone. With such standalone C example, you could easily test the 
results with different optimizations and compilers, it is easier to 
analyze, and easier to produce a bug report for GCC. What would make it 
harder in this case is that it needs special hardware, but you could 
still try with the example, and worry about that later (one option is 
running in an emulator, and again a standalone example really helps 
here). In principle, as it needs special hardware, the chances someone 
else would do this work is smaller. Indeed, if it turns out to be (3), 
it is unlikely to get resolved, but at least would get isolated (you 
would know what not to run).

As a user, if you run into a problem like this and do not want to get it 
fixed, but just work it around somehow. First, it may be dangerous, 
possibly one would get incorrect results from computations, but say in 
applications where they are verified externally. You could try disabling 
individual specific optimization until the tests pass. You could try 
with later versions of gcc-11 (even unreleased) or gcc-12. Still, a lot 
of this is easier with a small example, too. You could ignore the 
failing test. And it may not be worth it - it may be that you could get 
your speedups in a different, but more reliable way.

Using wsl2 on its own should not necessarily be a problem and the way 
you built gcc from the description should be ok, but at some point it 
would be worth checking under Linux and running natively - because even 
if these are numerical differences, they could be in principle caused by 
running on Windows (or in wsl2), at least in the past such differences 
were seen (related to (2) above). I would recommend checking on Linux 
natively once you have at least a standalone R example.

Best
Tomas

>
> best regards,
> Kieran
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel

Hi Tomas,

Many thanks for your thorough response, it is very much appreciated and
what you say makes perfect sense to me.

I was relying on the in-built R compilation checks, I have been working on
the assumption that everything on the R side is correct (including the
matrix package).

Indeed, R 4.1.3 builds and "make check-all" passes with the more
general -march=x86-64 architecture compiled with -O3 optimizations (in my
hands, on the Zen3 system). So I had no underlying reason not to believe R
or its packages were the problem when -march=znver3 was trialed. I found it
interesting that it was only the one factorizing.R script in the Matrix
suite that failed (out of the seemingly hundreds of remaining checks
overall which passed). So I was more wondering if there might have been
prior knowledge within the brain's trust on this list that "oh the
factorizing.R matrix test does ABC error when R or the package is compiled
with GCC using XYZ flags". As you'll read ahead, you can say that now.
:)

I don't think I have the capability to determine the root trigger in R
itself, the package, or the compiler (whichever one, or combination,  it
actually is). However, assuming R isn't the issue, I have done is go
through the GCC optimizations and I have now isolated the culprit
optimization which crashes factorizing.R.

It is "-fexpensive-optimizations".

If I use "-fno-expensive-optimizations" paired with -O2 or -O3
optimizations, all "make check-all" checks pass. So I can build a
fully
checked and passed R 4.1.3 under my environment now with:

~/R/R-4.1.3/configure CC=gcc-11.2 CXX=g++-11.2 FC=gfortran-11.2
CXXFLAGS="-O3 -march=znver3 -fno-expensive-optimizations -flto"
CFLAGS="-O3
-march=znver3 -fno-expensive-optimizations -flto" FFLAGS="-O3
-march=znver3
-fno-expensive-optimizations -flto" --enable-memory-profiling
--enable-R-shlib

I'm yet to benchmark whether the loss of that particular optimization flag
negates the advantages of using znver3 as a core architecture target over a
-x86-64 target in the first place.
So I think I've solved my own problem (at least, it appears that way based
on the checks).
So the remaining question is, what method or package does the development
team use (if any) for testing the speed of various base R calculations?

best regards,
Kieran

On Wed, Apr 13, 2022 at 4:00 PM Tomas Kalibera <tomas.kalibera at
gmail.com>
wrote:
> Hi Kieran,
>
> On 4/12/22 02:36, Kieran Short wrote:
> > Hello,
> >
> > I'm new to this list, and have subscribed particularly because
I've come
> > across an issue with building R from source with an AMD-based Zen
> > architecture under GCC11. Please don't attack me for my linux
operating
> > system choice, but it is Ubuntu 20.04 with Linux Kernel 5.10.102.1 -
> > microsoft-standard-WSL2. I've built GCC11 using GCC8 (the standard
GCC
> > under Ubuntu20.04 WSL release), under Windows11 with wslg. WSL2/g runs
> as a
> > hypervisor with ports to all system resources including display, GPU
> (cuda,
> > etc).
> >
> > The reason why I am posting this email is that I am trying to compile
R
> > using the AMD Zen3 platform architecture rather than x86/64, because
it
> has
> > processor-specific optimizations that improve performance over the
> standard
> > x86/64 in benchmarks. The Zen3 architecture optimizations are not
> available
> > in earlier versions of GCC (actually, they have possibly been
backported
> to
> > GCC10 now). Since Ubuntu 20.04 doesn't have GCC11, I compiled the
GCC11
> > compiler using the native GCC8.
> >
> > The GCC11 I have built can build R 4.1.3 with a standard x86-64
> > architecture and pass all tests with "make check-all".
> > I configured that with:
> >> ~/R/R-4.1.3/configure CC=gcc-11.2 CXX=g++-11.2 FC=gfortran-11.2
> > CXXFLAGS="-O3 -march=x86-64" CFLAGS="-O3
-march=x86-64" FFLAGS="-O3
> > -march=x86-64" --enable-memory-profiling --enable-R-shlib
> > and built with
> >> make -j 32 -O
> >> make check-all
> > ## PASS.
> >
> > So I can build R in my environment with GCC11.
> > In configure, I am using references to "gcc-11.2"
"gfortran-11.2" and
> > "g++-11.2" because I compiled GCC11 compilers with these
suffixes.
> >
> > Now, I'm using a 32 thread (16 core) AMD Zen3 CPU (a 5950x), and
want to
> > use it to its full potential. Zen3 optimizations are available as a
> > -march=znver3 option n GCC11. The znver3 optimizations improve
> performance
> > in Phoronix Test Suite benchmarks (I'm not aware of anyone that
has
> > compiled R with them). See:
> >
https://www.phoronix.com/scan.php?page=article&item=amd-5950x-gcc11
> >
> > However, the R 4.1.3 build (made with "make -j 32 -O"),
configured with
> > -march=znver3, produces an R that fails "make check-all".
> >
> >> ~/R/R-4.1.3/configure CC=gcc-11.2 CXX=g++-11.2 FC=gfortran-11.2
> > CXXFLAGS="-O2 -march=znver3" CFLAGS="-O2
-march=znver3" FFLAGS="-O2
> > -march=znver3" --enable-memory-profiling --enable-R-shlib
> > or
> >> ~/R/R-4.1.3/configure CC=gcc-11.2 CXX=g++-11.2 FC=gfortran-11.2
> > CXXFLAGS="-O3 -march=znver3" CFLAGS="-O3
-march=znver3" FFLAGS="-O3
> > -march=znver3" --enable-memory-profiling --enable-R-shlib
> >
> > The fail is always in the factorizing.R Matrix.R tests, and in
> particular,
> > there are a number of errors and a fatal error.
> > I have attached the output because I cannot really understand what is
> going
> > wrong. But results returned from matrix calculations are obviously odd
> with
> > -march=znver3 in GCC 11. There is another backwards-compatible
> architecture
> > option "znver2" and this has EXACTLY the same result.
> >
> > While there are other warrnings and errors (many in assert.EQ() ), the
> > factorizing.R script continues. The fatal error (at line 2662 in the
> > attached factorizing.Rout.fail text file) is:
> >
> >> ## problematic rank deficient rankMatrix() case -- only seen in
large
> > cases ??
> >> Z. <- readRDS(system.file("external",
"Z_NA_rnk.rds", package="Matrix"))
> >> tools::assertWarning(rnkZ. <- rankMatrix(Z., method =
"qr")) # gave
> errors
> > Error in assertCondition(expr, classes, .exprString = d.expr) :
> >    Failed to get warning in evaluating rnkZ. <- rankMatrix(Z.,
method
> ...
> > Calls: <Anonymous> -> assertCondition
> > Execution halted
> >
> > Can anybody shed light on what might be going on here? 'make
check-all'
> > passes all the other checks. It is just factorizing.R in Matrix that
> fails
> > (other matrix tests run ok).
> > Sorry this is a bit long-winded, but I thought details might be
> important.
>
> R gets used and tested most with the default optimizations, without use
> of model-specific instructions and with -O2 (GCC). It happens time to
> time that some people try other optimization options and run into
> problems. In principle, there are these cases (seen before):
>
> (1) the test in R package (or R) is wrong - it (unintentionally) expects
> behavior which has been observed in builds with default optimizations,
> but is not necessarily the only correct one; in case of numerical
> tolerances set empirically, they could simply be too tight
>
> (2) the algorithm in R package or R has a bug - the result is really
> wrong and it is because the algorithm is (unintentionally) not portable
> enough, it (unintentionally) only works with default optimizations or
> lower; in case of numerical results, this can be because it expects more
> precision from the floating point computations than mandated by IEEE, or
> assumes behavior not mandated
>
> (3) the optimization by design violates some properties the algorithm
> knowingly depends on; with numerical computations, this can be a sort of
> "fast" (and similarly referred to) mode which violates IEEE
floating
> point standard by design, in the aim of better performance; due to the
> nature of the algorithm depending on IEEE, and poor luck, the results
> end up completely wrong
>
> (4) there is a bug in the C or Fortran compiler (GCC as we use GCC) that
> only exhibits with the unusual optimizations; the compiler produces
> wrong code
>
> So, when you run into a problem like this and want to get that fixed,
> the first thing is to identify which case of the above it is, in case of
> 1 and 2 also differentiate between base R and a package (and which
> concrete package). Different people maintain these things and you would
> ideally narrow down the problem to a very small, isolated, reproducible
> example to support your claim where the bug is. If you do this right,
> the problem can often get fixed very fast.
>
> Such an example for (1) could be: few lines of standalone R code using
> Matrix that produces correct results, but the test is not happy. With
> pointers to the real check in the tests that is wrong. And an
> explanation why the result is wrong.
>
> For (2)-(4) it would be a minimal standalone C/Fortran example including
> only the critical function/part of algorithm that is not correct/not
> portable/not compiled correctly, with results obtained with
> optimizations where it works and where it doesn't. Unless you find an
> obvious bug in R easy to explain (2), when the example would not have to
> be standalone. With such standalone C example, you could easily test the
> results with different optimizations and compilers, it is easier to
> analyze, and easier to produce a bug report for GCC. What would make it
> harder in this case is that it needs special hardware, but you could
> still try with the example, and worry about that later (one option is
> running in an emulator, and again a standalone example really helps
> here). In principle, as it needs special hardware, the chances someone
> else would do this work is smaller. Indeed, if it turns out to be (3),
> it is unlikely to get resolved, but at least would get isolated (you
> would know what not to run).
>
> As a user, if you run into a problem like this and do not want to get it
> fixed, but just work it around somehow. First, it may be dangerous,
> possibly one would get incorrect results from computations, but say in
> applications where they are verified externally. You could try disabling
> individual specific optimization until the tests pass. You could try
> with later versions of gcc-11 (even unreleased) or gcc-12. Still, a lot
> of this is easier with a small example, too. You could ignore the
> failing test. And it may not be worth it - it may be that you could get
> your speedups in a different, but more reliable way.
>
> Using wsl2 on its own should not necessarily be a problem and the way
> you built gcc from the description should be ok, but at some point it
> would be worth checking under Linux and running natively - because even
> if these are numerical differences, they could be in principle caused by
> running on Windows (or in wsl2), at least in the past such differences
> were seen (related to (2) above). I would recommend checking on Linux
> natively once you have at least a standalone R example.
>
> Best
> Tomas
>
>
> >
> > best regards,
> > Kieran
> > ______________________________________________
> > R-devel at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-devel
>
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