forked from OSchip/llvm-project
570c50aa92
lib/Transform/ScheduleOptimizer.cpp fails to compile on Solaris, both on the 9.x
branch (first noticed when running test-release.sh without -no-polly) and on trunk:
/var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/tools/polly/lib/Transform/ScheduleOptimizer.cpp: In function ‘MicroKernelParamsTy getMicroKernelParams(const llvm::TargetTransformInfo*, polly::MatMulInfoTy)’:
/var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/tools/polly/lib/Transform/ScheduleOptimizer.cpp:914:62: error: call of overloaded ‘sqrt(long unsigned int)’ is ambiguous
914 | ceil(sqrt(Nvec * LatencyVectorFma * ThroughputVectorFma) / Nvec) * Nvec;
| ^
In file included from /usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/math.h:24,
from /usr/gcc/9/include/c++/9.1.0/cmath:45,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm-c/DataTypes.h:28,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm/Support/DataTypes.h:16,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm/ADT/Hashing.h:47,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm/ADT/ArrayRef.h:12,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/tools/polly/include/polly/ScheduleOptimizer.h:12,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/tools/polly/lib/Transform/ScheduleOptimizer.cpp:48:
/usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/iso/math_iso.h:220:21: note: candidate: ‘long double std::sqrt(long double)’
220 | inline long double sqrt(long double __X) { return __sqrtl(__X); }
| ^~~~
/usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/iso/math_iso.h:186:15:
note: candidate: ‘float std::sqrt(float)’
186 | inline float sqrt(float __X) { return __sqrtf(__X); }
| ^~~~
/usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/iso/math_iso.h:74:15:
note: candidate: ‘double std::sqrt(double)’
74 | extern double sqrt __P((double));
| ^~~~
/var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/tools/polly/lib/Transform/ScheduleOptimizer.cpp:915:67:
error: call of overloaded ‘ceil(long unsigned int)’ is ambiguous
915 | int Mr = ceil(Nvec * LatencyVectorFma * ThroughputVectorFma / Nr);
| ^
In file included from /usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/math.h:24,
from /usr/gcc/9/include/c++/9.1.0/cmath:45,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm-c/DataTypes.h:28,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm/Support/DataTypes.h:16,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm/ADT/Hashing.h:47,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/include/llvm/ADT/ArrayRef.h:12,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/tools/polly/include/polly/ScheduleOptimizer.h:12,
from /var/llvm/llvm-9.0.0-rc4/rc4/llvm.src/tools/polly/lib/Transform/ScheduleOptimizer.cpp:48:
/usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/iso/math_iso.h:196:21: note: candidate: ‘long double std::ceil(long double)’
196 | inline long double ceil(long double __X) { return __ceill(__X); }
| ^~~~
/usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/iso/math_iso.h:160:15:
note: candidate: ‘float std::ceil(float)’
160 | inline float ceil(float __X) { return __ceilf(__X); }
| ^~~~
/usr/gcc/9/lib/gcc/x86_64-pc-solaris2.11/9.1.0/include-fixed/iso/math_iso.h:76:15:
note: candidate: ‘double std::ceil(double)’
76 | extern double ceil __P((double));
| ^~~~
Fixed by adding casts to disambiguate, checked that it now compiles on both
amd64-pc-solaris2.11 and sparcv9-sun-solaris2.11 and on x86_64-pc-linux-gnu.
Differential Revision: https://reviews.llvm.org/D67442
llvm-svn: 371825
|
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|---|---|---|
| .. | ||
| cmake | ||
| docs | ||
| include/polly | ||
| lib | ||
| test | ||
| tools | ||
| unittests | ||
| utils | ||
| www | ||
| .arcconfig | ||
| .arclint | ||
| .gitattributes | ||
| .gitignore | ||
| CMakeLists.txt | ||
| CREDITS.txt | ||
| LICENSE.txt | ||
| README | ||
README
Polly - Polyhedral optimizations for LLVM ----------------------------------------- http://polly.llvm.org/ Polly uses a mathematical representation, the polyhedral model, to represent and transform loops and other control flow structures. Using an abstract representation it is possible to reason about transformations in a more general way and to use highly optimized linear programming libraries to figure out the optimal loop structure. These transformations can be used to do constant propagation through arrays, remove dead loop iterations, optimize loops for cache locality, optimize arrays, apply advanced automatic parallelization, drive vectorization, or they can be used to do software pipelining.