Commit b797dcb5 authored by Jakub Klinkovský's avatar Jakub Klinkovský
Browse files

Fixed math functions in reduction-operations.h 

Apparently there is an abs function defined in <cstdlib>, which works
only on integers. We want the std::abs overloads defined in <cmath>,
which are used by TNL::abs.

clang rocks!

http://stackoverflow.com/questions/29136607/c-what-is-wrong-with-abs#29136774
parent c7691e18

src/TNL/Containers/Algorithms/reduction-operations.h

+12 −12
Original line number Diff line number Diff line
@@ -76,7 +76,7 @@ class tnlParallelReductionMin 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return min( current, data1[ idx ] );

      return TNL::min( current, data1[ idx ] );

   };



   __cuda_callable__ ResultType initialValue() { return MaxValue< ResultType>(); };
@@ -117,7 +117,7 @@ class tnlParallelReductionMax 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return max( current, data1[ idx ] );

      return TNL::max( current, data1[ idx ] );

   };



   __cuda_callable__ ResultType initialValue() { return MinValue< ResultType>(); };
@@ -241,7 +241,7 @@ class tnlParallelReductionAbsSum : public tnlParallelReductionSum< Real, Index > 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return current + ::abs( data1[ idx ] );

      return current + TNL::abs( data1[ idx ] );

   };



   __cuda_callable__ ResultType initialValue() { return ( ResultType ) 0; };
@@ -270,7 +270,7 @@ class tnlParallelReductionAbsMin : public tnlParallelReductionMin< Real, Index > 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return min( current, abs( data1[ idx ] ) );

      return TNL::min( current, TNL::abs( data1[ idx ] ) );

   };



   __cuda_callable__ ResultType initialValue() { return MaxValue< ResultType>(); };
@@ -299,7 +299,7 @@ class tnlParallelReductionAbsMax : public tnlParallelReductionMax< Real, Index > 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return std::max( current, ::abs( data1[ idx ] ) );

      return std::max( current, TNL::abs( data1[ idx ] ) );

   };



   __cuda_callable__ ResultType initialValue() { return ( ResultType ) 0; };
@@ -365,7 +365,7 @@ class tnlParallelReductionLpNorm : public tnlParallelReductionSum< Real, Index > 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return current + ::pow( ::abs( data1[ idx ] ), p );

      return current + TNL::pow( TNL::abs( data1[ idx ] ), p );

   };



   __cuda_callable__ ResultType initialValue() { return ( ResultType ) 0; };
@@ -514,7 +514,7 @@ class tnlParallelReductionDiffMin : public tnlParallelReductionMin< Real, Index 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return min( current, data1[ idx ] - data2[ idx ] );

      return TNL::min( current, data1[ idx ] - data2[ idx ] );

   };



   __cuda_callable__ ResultType initialValue() { return MaxValue< ResultType>(); };
@@ -543,7 +543,7 @@ class tnlParallelReductionDiffMax : public tnlParallelReductionMax< Real, Index 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return max( current, data1[ idx ] - data2[ idx ] );

      return TNL::max( current, data1[ idx ] - data2[ idx ] );

   };



   __cuda_callable__ ResultType initialValue() { return ( ResultType ) 0; };
@@ -572,7 +572,7 @@ class tnlParallelReductionDiffAbsSum : public tnlParallelReductionMax< Real, Ind 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return current + abs( data1[ idx ] - data2[ idx ] );

      return current + TNL::abs( data1[ idx ] - data2[ idx ] );

   };



   __cuda_callable__ ResultType initialValue() { return ( ResultType ) 0; };
@@ -601,7 +601,7 @@ class tnlParallelReductionDiffAbsMin : public tnlParallelReductionMin< Real, Ind 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return min( current, abs( data1[ idx ] - data2[ idx ] ) );

      return TNL::min( current, TNL::abs( data1[ idx ] - data2[ idx ] ) );

   };



   __cuda_callable__ ResultType initialValue() { return MaxValue< ResultType>(); };
@@ -630,7 +630,7 @@ class tnlParallelReductionDiffAbsMax : public tnlParallelReductionMax< Real, Ind 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return max( current, abs( data1[ idx ] - data2[ idx ] ) );

      return TNL::max( current, TNL::abs( data1[ idx ] - data2[ idx ] ) );

   };



   __cuda_callable__ ResultType initialValue() { return ( ResultType ) 0; };
@@ -699,7 +699,7 @@ class tnlParallelReductionDiffLpNorm : public tnlParallelReductionSum< Real, Ind 
                            const RealType* data1,

                            const RealType* data2 )

   {

      return current + ::pow( abs( data1[ idx ] - data2[ idx ] ), p );

      return current + TNL::pow( TNL::abs( data1[ idx ] - data2[ idx ] ), p );

   };



   __cuda_callable__ ResultType initialValue() { return ( ResultType ) 0; };