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Bug summary If fast-math is enabled, the JIT compilation (on an NVIDIA GPU) fails when using std::pow inside std::for_each with:
std::pow
std::for_each
fatal error: error in backend: Undefined external symbol "__powidf2" [AdaptiveCpp Error] from adaptivecpp/source/include/hipSYCL/glue/llvm-sscp/jit.hpp:265 @ compile(): jit::compile: Encountered errors: 0: LLVMToPtx: clang invocation failed with exit code 70
The same also happens with std::exp:
std::exp
fatal error: error in backend: Undefined external symbol "exp" [AdaptiveCpp Error] adaptivecpp/source/include/hipSYCL/glue/llvm-sscp/jit.hpp:265 @ compile(): jit::compile: Encountered errors: 0: LLVMToPtx: clang invocation failed with exit code 70
To Reproduce Compile a code snippet using C++ standard parallelism on an NVIDIA GPU with AdaptiveCpp v24.02.0
Expected behavior JIT compilation should not fail.
The text was updated successfully, but these errors were encountered:
We should also look into supporting the functions here: https://gcc.gnu.org/onlinedocs/gccint/Soft-float-library-routines.html which is where the mentioned __powidf2 is listed.
__powidf2
Not sure though why these are specifically emitted with fast-math, seems as this is more related to emulating functions without hardware support.
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Bug summary
If fast-math is enabled, the JIT compilation (on an NVIDIA GPU) fails when using
std::pow
insidestd::for_each
with:The same also happens with
std::exp
:To Reproduce
Compile a code snippet using C++ standard parallelism on an NVIDIA GPU with AdaptiveCpp v24.02.0
Expected behavior
JIT compilation should not fail.
The text was updated successfully, but these errors were encountered: