diff --git a/src/Benchmarks/SpMV/spmv.h b/src/Benchmarks/SpMV/spmv.h
index 484ff2358932e5373cd21dccda07cf3dd580ae31..6a9dab96a4913e15a0f2e970a6480f88f5946404 100644
--- a/src/Benchmarks/SpMV/spmv.h
+++ b/src/Benchmarks/SpMV/spmv.h
@@ -59,8 +59,6 @@ std::string getMatrixFormat( const Matrix& matrix )
return format;
}
-// This function is not used currently (as of 17.03.19),
-// as the log takes care of printing and saving this information into the log file.
// Print information about the matrix.
template< typename Matrix >
void printMatrixInfo( const Matrix& matrix,
@@ -218,14 +216,6 @@ benchmarkSpMV( Benchmark & benchmark,
// Setup cuSPARSE MetaData, since it has the same header as CSR,
// and therefore will not get its own headers (rows, cols, speedup etc.) in log.
// * Not setting this up causes (among other undiscovered errors) the speedup from CPU to GPU on the input format to be overwritten.
-
- // FIXME: How to include benchmark with different name under the same header as the current format being benchmarked???
- // FIXME: Does it matter that speedup show difference only between current test and first test?
- // Speedup shows difference between CPU and GPU-cuSPARSE, because in Benchmarks.h:
- // * If there is no baseTime, the resulting test time is set to baseTime.
- // * However, if there is a baseTime (from the CPU compared to GPU test),
- // baseTime isn't changed. If we change it in Benchmarks.h to compare
- // the speedup from the last test, it will mess up BLAS benchmarks etc.
benchmark.setMetadataColumns( Benchmark::MetadataColumns({
{ "matrix name", convertToString( getMatrixFileName( inputFileName ) ) },
{ "non-zeros", convertToString( hostMatrix.getNumberOfNonzeroMatrixElements() ) },
@@ -244,7 +234,6 @@ benchmarkSpMV( Benchmark & benchmark,
resultcuSPARSEDeviceVector2 = deviceVector2;
#endif
-//#ifdef COMPARE_RESULTS
// Difference between GPU (curent format) and GPU-cuSPARSE results
Real cuSparseDifferenceAbsMax = resultDeviceVector2.differenceAbsMax( resultcuSPARSEDeviceVector2 );
Real cuSparseDifferenceLpNorm = resultDeviceVector2.differenceLpNorm( resultcuSPARSEDeviceVector2, 1 );
@@ -274,15 +263,6 @@ benchmarkSpMV( Benchmark & benchmark,
std::cout << GPUcuSparse_absMax << std::endl;
std::cout << GPUcuSparse_lpNorm << std::endl;
- // FIXME: This isn't an elegant solution, it makes the log file very long.
-// benchmark.addErrorMessage( GPUcuSparse_absMax, 1 );
-// benchmark.addErrorMessage( GPUcuSparse_lpNorm, 1 );
-
-// benchmark.addErrorMessage( CPUxGPU_absMax, 1 );
-// benchmark.addErrorMessage( CPUxGPU_lpNorm, 1 );
-
-//#endif
-
std::cout << std::endl;
return true;
}
@@ -295,15 +275,14 @@ benchmarkSpmvSynthetic( Benchmark & benchmark,
bool verboseMR )
{
bool result = true;
- // TODO: benchmark all formats from tnl-benchmark-spmv (different parameters of the base formats)
-// result |= benchmarkSpMV< Real, Matrices::CSR >( benchmark, inputFileName, verboseMR );
-// result |= benchmarkSpMV< Real, Matrices::Ellpack >( benchmark, inputFileName, verboseMR );
-// result |= benchmarkSpMV< Real, SlicedEllpack >( benchmark, inputFileName, verboseMR );
-// result |= benchmarkSpMV< Real, Matrices::ChunkedEllpack >( benchmark, inputFileName, verboseMR );
+ result |= benchmarkSpMV< Real, Matrices::CSR >( benchmark, inputFileName, verboseMR );
+ result |= benchmarkSpMV< Real, Matrices::Ellpack >( benchmark, inputFileName, verboseMR );
+ result |= benchmarkSpMV< Real, SlicedEllpack >( benchmark, inputFileName, verboseMR );
+ result |= benchmarkSpMV< Real, Matrices::ChunkedEllpack >( benchmark, inputFileName, verboseMR );
- // AdEllpack/BiEllpack doesn't have cross-device assignment ('= operator') implemented yet
- result |= benchmarkSpMV< Real, Matrices::AdEllpack >( benchmark, inputFileName, verboseMR );
-// result |= benchmarkSpMV< Real, Matrices::BiEllpack >( benchmark, inputFileName, verboseMR );
+ // AdEllpack is broken
+// result |= benchmarkSpMV< Real, Matrices::AdEllpack >( benchmark, inputFileName, verboseMR );
+ result |= benchmarkSpMV< Real, Matrices::BiEllpack >( benchmark, inputFileName, verboseMR );
return result;
}
diff --git a/src/TNL/Matrices/AdEllpack.h b/src/TNL/Matrices/AdEllpack.h
index 1fcbc1494d5cd133dcaad116b2c5efde33824515..34b081914604f949668d98a8ec5d7657c5ed4086 100644
--- a/src/TNL/Matrices/AdEllpack.h
+++ b/src/TNL/Matrices/AdEllpack.h
@@ -81,13 +81,12 @@ public:
std::cout << "HEAD==TAIL" << std::endl;
else
{
- // TEST
for( warpInfo< MatrixType >* i = this->getHead(); i != this->getTail()->next; i = i->next )
{
- if( i == this->getHead() );
-// std::cout << "Head:" << "\ti->localLoad = " << i->localLoad << "\ti->offset = " << i->offset << "\ti->rowOffset = " << i->rowOffset << std::endl;
- else if( i == this->getTail() );
-// std::cout << "Tail:" << "\ti->localLoad = " << i->localLoad << "\ti->offset = " << i->offset << "\ti->rowOffset = " << i->rowOffset << std::endl;
+ if( i == this->getHead() )
+ std::cout << "Head:" << "\ti->localLoad = " << i->localLoad << "\ti->offset = " << i->offset << "\ti->rowOffset = " << i->rowOffset << std::endl;
+ else if( i == this->getTail() )
+ std::cout << "Tail:" << "\ti->localLoad = " << i->localLoad << "\ti->offset = " << i->offset << "\ti->rowOffset = " << i->rowOffset << std::endl;
else
std::cout << "\ti->localLoad = " << i->localLoad << "\ti->offset = " << i->offset << "\ti->rowOffset = " << i->rowOffset << std::endl;
}
diff --git a/src/TNL/Matrices/AdEllpack_impl.h b/src/TNL/Matrices/AdEllpack_impl.h
index dd8731fc66e4029acc192b392a73e7fb804e9acc..b01e9041e6a79d12f5ab3755e76340ea9dba135f 100644
--- a/src/TNL/Matrices/AdEllpack_impl.h
+++ b/src/TNL/Matrices/AdEllpack_impl.h
@@ -147,10 +147,6 @@ warpList< MatrixType >::~warpList()
delete temp;
}
delete this->head;
-
- // TEST
-// std::cout << "List destructor." << std::endl;
-// this->printList();
}
@@ -1169,46 +1165,31 @@ void AdEllpack< Real, Device, Index >::spmvCuda8( const InVector& inVector,
IndexType i = 0;
IndexType elementPtr = this->offset[ warpIdx ] + inWarpIdx;
- // Save the value, to save calling access every loop.
const IndexType warpLoad = this->localLoad[ warpIdx ];
- // The unroll factor is 4, therefore if a warp has less than 4 localLoad, it cannot be unrolled
- // and must be calculated separately.
if( warpLoad < 4 )
{
- // While the helpful index of the warp localLoad is less than localLoad and the element index isn't
- // out of the matrix (would return the number of columns of the matrix)
while( i < warpLoad &&
this->columnIndexes[ elementPtr ] < this->getColumns() )
{
temp[ threadIdx.x ] += inVector[ this->columnIndexes[ elementPtr ] ] * this->values[ elementPtr ];
- // For the current thread, shift the elements ptr by warpSize (to keep the thread on one row)
elementPtr += this->warpSize;
- i++; // Increment the helpful localLoad index.
+ i++;
}
}
- else // If the localLoad of the warp is unrollable.
+ else
{
- // Is the warpLoad divisible by 4 (4 - 1 for binary AND).
- // This will return how far it is from being divisible:
- // For 0 & 3 = 0; 1 & 3 = 1; 2 & 3 = 2; 3 & 3 = 3; 4 & 3 = 0, etc.
IndexType alignUnroll = warpLoad & 3;
- // While the result of divisibility by 4 has not reached the point where it is divisble by 4.
while( alignUnroll != 0 &&
this->columnIndexes[ elementPtr ] < this->getColumns() )
{
temp[ threadIdx.x ] += inVector[ this->columnIndexes[ elementPtr ] ] * this->values[ elementPtr ];
elementPtr += this->warpSize;
i++;
- // If alignUnroll not 0 (i.e. no. of NNZ elements is not divisible by 4), decrement alignUnroll until it is.
- // This will ensure that the i starting index with be incremented to the correct starting position for the unroll.
alignUnroll--;
}
}
-
- // For those rows that have warpLoad < unroll factor, this for loop won't even get past the first condition.
- // Otherwise unroll.
for( ; i < this->localLoad[ warpIdx ]; i += 4 )
{
#pragma unroll
@@ -1222,7 +1203,6 @@ void AdEllpack< Real, Device, Index >::spmvCuda8( const InVector& inVector,
}
}
- // What is going on here? DOCUMENT
if( ( inWarpIdx == 0 ) || ( reduceMap[ threadIdx.x ] > reduceMap[ threadIdx.x - 1 ] ) )
{
IndexType end = ( warpIdx + 1 ) << 5;
@@ -1265,15 +1245,6 @@ void AdEllpack< Real, Device, Index >::spmvCuda16( const InVector& inVector,
IndexType elementPtr = this->offset[ warpIdx ] + inWarpIdx;
const IndexType warpLoad = this->localLoad[ warpIdx ];
-// for( IndexType i = 0; i < warpLoad; i++ )
-// {
-// if( this->columnIndexes[ elementPtr ] < this->getColumns() )
-// {
-// temp[ threadIdx.x ] += this->values[ elementPtr] * inVector[ this->columnIndexes[ elementPtr ] ];
-// elementPtr += this->warpSize;
-// }
-// }
-
if( warpLoad < 8 )
{
while( i < warpLoad &&
@@ -1496,7 +1467,6 @@ public:
InVector* kernel_inVector = Devices::Cuda::passToDevice( inVector );
OutVector* kernel_outVector = Devices::Cuda::passToDevice( outVector );
TNL_CHECK_CUDA_DEVICE;
- std::cout << "totalLoad = " << matrix.totalLoad << std::endl;
if( matrix.totalLoad < 2 )
{
dim3 blockSize( 256 ), cudaGridSize( Cuda::getMaxGridSize() );
@@ -1520,7 +1490,7 @@ public:
Cuda::freeFromDevice( kernel_outVector );
TNL_CHECK_CUDA_DEVICE;
}
- else if( matrix.totalLoad < 4 ) // WORKS
+ else if( matrix.totalLoad < 4 )
{
dim3 blockSize( 192 ), cudaGridSize( Cuda::getMaxGridSize() );
IndexType cudaBlocks = roundUpDivision( matrix.reduceMap.getSize(), blockSize.x );
@@ -1543,7 +1513,7 @@ public:
Cuda::freeFromDevice( kernel_outVector );
TNL_CHECK_CUDA_DEVICE;
}
- else if( matrix.totalLoad < 8 ) // Maybe works?
+ else if( matrix.totalLoad < 8 )
{
dim3 blockSize( 128 ), cudaGridSize( Cuda::getMaxGridSize() );
IndexType cudaBlocks = roundUpDivision( matrix.reduceMap.getSize(), blockSize.x );
@@ -1566,7 +1536,7 @@ public:
Devices::Cuda::freeFromDevice( kernel_outVector );
TNL_CHECK_CUDA_DEVICE;
}
- else if( matrix.totalLoad < 16 ) // BROKEN
+ else if( matrix.totalLoad < 16 )
{
dim3 blockSize( 128 ), cudaGridSize( Cuda::getMaxGridSize() );
IndexType cudaBlocks = roundUpDivision( matrix.reduceMap.getSize(), blockSize.x );
@@ -1589,7 +1559,7 @@ public:
Cuda::freeFromDevice( kernel_outVector );
TNL_CHECK_CUDA_DEVICE;
}
- else // BROKEN
+ else
{
dim3 blockSize( 96 ), cudaGridSize( Cuda::getMaxGridSize() );
IndexType cudaBlocks = roundUpDivision( matrix.reduceMap.getSize(), blockSize.x );
@@ -1606,7 +1576,7 @@ public:
kernel_outVector,
gridIdx );
}
- TNL_CHECK_CUDA_DEVICE; // FREEZES right here on CHECK CUDA
+ TNL_CHECK_CUDA_DEVICE;
Devices::Cuda::freeFromDevice( kernel_this );
Devices::Cuda::freeFromDevice( kernel_inVector );
Devices::Cuda::freeFromDevice( kernel_outVector );
diff --git a/src/TNL/Matrices/BiEllpack_impl.h b/src/TNL/Matrices/BiEllpack_impl.h
index bfb8a7e35aa677a1af434efea16d1982b27ec3ca..e20b5cd2307f0b749f18ebd252ab2e55c41422f9 100644
--- a/src/TNL/Matrices/BiEllpack_impl.h
+++ b/src/TNL/Matrices/BiEllpack_impl.h
@@ -802,10 +802,9 @@ template< typename Real,
void BiEllpack< Real, Device, Index >::printValues() const
{
for( Index i = 0; i < this->values.getSize(); i++ ) {
- // Random values are stored with the column index of getColumns(). e.g. a matrix has 4 columns, values are at column indexes 0, 1, 2, 3 and junk data at index 4.
- if( this->columnIndexes.getElement( i ) != this->getColumns() )
- std::cout << "values.getElement( " << i << " ) = " << this->values.getElement( i )
- << "\tcolumnIndexes.getElement( " << i << " ) = " << this->columnIndexes.getElement( i ) << std::endl;
+ if( this->columnIndexes.getElement( i ) != this->getColumns() )
+ std::cout << "values.getElement( " << i << " ) = " << this->values.getElement( i )
+ << "\tcolumnIndexes.getElement( " << i << " ) = " << this->columnIndexes.getElement( i ) << std::endl;
}
for( Index i = 0; i < this->rowPermArray.getSize(); i++ ) {
diff --git a/src/TNL/Matrices/CSR_impl.h b/src/TNL/Matrices/CSR_impl.h
index 8891f5b935ece70662c2a17d2e7ca67c508af6aa..3164a7fff39bd9095f39b606cad43b8d32e7b898 100644
--- a/src/TNL/Matrices/CSR_impl.h
+++ b/src/TNL/Matrices/CSR_impl.h
@@ -124,41 +124,8 @@ template< typename Real,
Index CSR< Real, Device, Index >::getNonZeroRowLength( const IndexType row ) const
{
// TODO: Fix/Implement
- throw Exceptions::NotImplementedError( "CSR::getNonZeroRowLength is not implemented." );
-// if( std::is_same< DeviceType, Devices::Host >::value )
-// {
-// ConstMatrixRow matrixRow = this->getRow( row );
-// return matrixRow.getNonZeroElementsCount();
-// }
-// if( std::is_same< DeviceType, Devices::Cuda >::value )
-// {
-// IndexType *cols = new IndexType[4];
-// RealType *vals = new RealType[4];
-// for( int i = 0; i < 4; i++ )
-// {
-// cols[i] = i;
-// vals[i] = 1.0;
-// }
-// ConstMatrixRow matrixRow(cols, vals, 4, 1);
-// // ConstMatrixRow matrixRow = this->getRow( row );// If the program even compiles, this line fails because a segfault is thrown on the first line of getRow()
-// // WHEN debugging with GDB:
-// // (gdb) p this->rowPointers[0]
-// // Could not find operator[].
-// // (gdb) p rowPointers.getElement(0)
-// // Attempt to take address of value not located in memory.
-// IndexType resultHost ( 0 );
-// IndexType* resultCuda = Cuda::passToDevice( resultHost );
-// // PROBLEM: If the second parameter of getNonZeroRowLengthCudaKernel is '&resultCuda', the following issue is thrown:
-// // 'error: no instance of function template "TNL::Matrices::getNonZeroRowLengthCudaKernel" matches the argument list'
-// TNL::Matrices::getNonZeroRowLengthCudaKernel< ConstMatrixRow, IndexType ><<< 1, 1 >>>( matrixRow, resultCuda ); // matrixRow works fine, tested them both separately
-// delete []cols;
-// delete []vals;
-// std::cout << "Checkpoint BEFORE passFromDevice" << std::endl;
-// resultHost = Cuda::passFromDevice( resultCuda ); // This causes a crash: Illegal memory address in Cuda_impl.h at TNL_CHECK_CUDA_DEVICE
-// std::cout << "Checkpoint AFTER passFromDevice" << std::endl;
-// Cuda::freeFromDevice( resultCuda );
-// return resultHost;
-// }
+ TNL_ASSERT( false, std::cerr << "TODO: Fix/Implement" );
+ return 0;
}
template< typename Real,
@@ -223,13 +190,6 @@ bool CSR< Real, Device, Index >::addElementFast( const IndexType row,
const RealType& value,
const RealType& thisElementMultiplicator )
{
- /*TNL_ASSERT( row >= 0 && row < this->rows &&
- column >= 0 && column <= this->rows,
- std::cerr << " row = " << row
- << " column = " << column
- << " this->rows = " << this->rows
- << " this->columns = " << this-> columns );*/
-
IndexType elementPtr = this->rowPointers[ row ];
const IndexType rowEnd = this->rowPointers[ row + 1 ];
IndexType col = 0;
diff --git a/src/TNL/Matrices/ChunkedEllpack_impl.h b/src/TNL/Matrices/ChunkedEllpack_impl.h
index 32cfca2c4e68dea5a1ebc0c99753f1890eeacf9b..3826a8574fc302d86f363f1d7e72d58d89526303 100644
--- a/src/TNL/Matrices/ChunkedEllpack_impl.h
+++ b/src/TNL/Matrices/ChunkedEllpack_impl.h
@@ -275,22 +275,7 @@ template< typename Real,
Index ChunkedEllpack< Real, Device, Index >::getNonZeroRowLength( const IndexType row ) const
{
ConstMatrixRow matrixRow = getRow( row );
- return matrixRow.getNonZeroElementsCount( getType< Device >() );
-
-// IndexType elementCount ( 0 );
-// ConstMatrixRow matrixRow = this->getRow( row );
-//
-// auto computeNonZeros = [&] /*__cuda_callable__*/ ( IndexType i ) mutable
-// {
-// std::cout << "matrixRow.getElementValue( i ) = " << matrixRow.getElementValue( i ) << " != 0.0" << std::endl;
-// if( matrixRow.getElementValue( i ) != 0.0 )
-// elementCount++;
-//
-// std::cout << "End of lambda elementCount = " << elementCount << std::endl;
-// };
-//
-// ParallelFor< DeviceType >::exec( ( IndexType ) 0, matrixRow.getLength(), computeNonZeros );
-// return elementCount;
+ return matrixRow.getNonZeroElementsCount( Device::getDeviceType() );
}
template< typename Real,
diff --git a/src/TNL/Matrices/Ellpack_impl.h b/src/TNL/Matrices/Ellpack_impl.h
index d92bccc46b4908708b608bff7e36b974dcbf7131..b99dbc88b59d425e93cc7cde2ee3fe9f31064b0a 100644
--- a/src/TNL/Matrices/Ellpack_impl.h
+++ b/src/TNL/Matrices/Ellpack_impl.h
@@ -655,8 +655,6 @@ Ellpack< Real, Device, Index >::operator=( const Ellpack< Real2, Device2, Index2
// setLike does not work here due to different alignment on Cuda and Host
this->rowLengths = matrix.rowLengths;
this->setDimensions( matrix.getRows(), matrix.getColumns() );
-
-// std::cout << "DIMENSIONS set; after setDimensions in operator= cross-device" << std::endl;
const int blockSize = 32;
const int blocks = roundUpDivision( this->getRows(), blockSize );
diff --git a/src/TNL/Matrices/MatrixReader_impl.h b/src/TNL/Matrices/MatrixReader_impl.h
index b3fb33856a812a0ff82eb26d134f411c62d5ed2f..dd6ddc07245150c859946e97b2fde3a66021aaa3 100644
--- a/src/TNL/Matrices/MatrixReader_impl.h
+++ b/src/TNL/Matrices/MatrixReader_impl.h
@@ -68,14 +68,8 @@ bool MatrixReader< Matrix >::readMtxFileHostMatrix( std::istream& file,
if( ! computeCompressedRowLengthsFromMtxFile( file, rowLengths, columns, rows, symmetricMatrix, verbose ) )
return false;
-
-// std::cout << " rowLengths sizeof: " << sizeof( rowLengths ) << std::endl;
-// std::cout << " rowLengths element sizeof: " << sizeof( rowLengths[0] ) << std::endl;
-// std::cout << " rowLengths getSize(): " << rowLengths.getSize() << std::endl;
matrix.setCompressedRowLengths( rowLengths );
-
-// std::cout << "->CompressedRowLengths SET" << std::endl;
if( ! readMatrixElementsFromMtxFile( file, matrix, symmetricMatrix, verbose, symReader ) )
return false;
@@ -347,8 +341,6 @@ bool MatrixReader< Matrix >::readMatrixElementsFromMtxFile( std::istream& file,
Timer timer;
timer.start();
-// std::cout << "\nBefore while..." << std::endl;
-
while( std::getline( file, line ) )
{
if( line[ 0 ] == '%' ) continue;
@@ -380,8 +372,6 @@ bool MatrixReader< Matrix >::readMatrixElementsFromMtxFile( std::istream& file,
}
}
-// std::cout << "\nAfter while..." << std::endl;
-
file.clear();
long int fileSize = file.tellg();
timer.stop();
@@ -390,8 +380,6 @@ bool MatrixReader< Matrix >::readMatrixElementsFromMtxFile( std::istream& file,
<< " -> " << timer.getRealTime()
<< " sec. i.e. " << fileSize / ( timer.getRealTime() * ( 1 << 20 )) << "MB/s." << std::endl;
-// std::cout << "->END of reading matrix elements from file" << std::endl;
-
return true;
}
diff --git a/src/TNL/Matrices/Sparse_impl.h b/src/TNL/Matrices/Sparse_impl.h
index 84d734a937bea3575ecaf737f14d400585fcca92..d1643db19a48dbf078fe04389e9cb2d061b28a26 100644
--- a/src/TNL/Matrices/Sparse_impl.h
+++ b/src/TNL/Matrices/Sparse_impl.h
@@ -109,19 +109,7 @@ template< typename Real,
typename Index >
void Sparse< Real, Device, Index >::allocateMatrixElements( const IndexType& numberOfMatrixElements )
{
-// std::cout << " Allocating matrix elements..." << std::endl;
- // CHECKING: if the number of matrix elements is larger than the highest number the IndexType can go to?
- // INT OVERFLOW
-
- // CORRECT? ELL stores certain matrices in such a way, which could cause the number of matrix elements
- // to be greater than the maximum value IndexType can store, thus causing int overflow when
- // creating the arrays "values" and "indexes".
- // PROBLEM: int can overflow in such a way that it is still positive, thus rendering this assert useless.
- // HOW FIX? Do we have to create special conditions for every format in its allocation method? We can't
- // tell from within this method, if numberOfMatrixElements is an overflown value or not.
TNL_ASSERT_GE( numberOfMatrixElements, 0, "Number of matrix elements must be non-negative." );
-
-// std::cout << " numberOfMatrixElements = " << numberOfMatrixElements << std::endl;
this->values.setSize( numberOfMatrixElements );
this->columnIndexes.setSize( numberOfMatrixElements );
@@ -132,8 +120,6 @@ void Sparse< Real, Device, Index >::allocateMatrixElements( const IndexType& num
*/
if( numberOfMatrixElements > 0 )
this->columnIndexes.setValue( this->columns );
-
-// std::cout << "->END OF allocateMatrixElements!!!" << std::endl;
}
template< typename Real,
diff --git a/src/UnitTests/Matrices/DenseMatrixTest.h b/src/UnitTests/Matrices/DenseMatrixTest.h
index e870cd90545656d005066eade0c5f2a07773798b..8d9e9c727a0d88c40b90f0623d8b2ec8808e3f95 100644
--- a/src/UnitTests/Matrices/DenseMatrixTest.h
+++ b/src/UnitTests/Matrices/DenseMatrixTest.h
@@ -41,17 +41,14 @@ void host_test_GetType()
EXPECT_EQ( mtrxHostInt.getType(), TNL::String( "Matrices::Dense< int, Devices::Host, int >" ) );
}
-// QUESITON: Cant these two functions be combined into one? Because if no CUDA is present and we were to call
-// CUDA into the function in the TEST, to be tested, then we could have a problem.
-
template< typename MatrixCudaFloat, typename MatrixCudaInt >
void cuda_test_GetType()
{
MatrixCudaFloat mtrxCudaFloat;
MatrixCudaInt mtrxCudaInt;
- EXPECT_EQ( mtrxCudaFloat.getType(), TNL::String( "Matrices::Dense< float, Devices::Cuda, int >" ) ); // This is mistakenly labeled in /src/TNL/Devices/Cuda.cpp
- EXPECT_EQ( mtrxCudaInt.getType(), TNL::String( "Matrices::Dense< int, Devices::Cuda, int >" ) ); // Should be Devices::Cuda
+ EXPECT_EQ( mtrxCudaFloat.getType(), TNL::String( "Matrices::Dense< float, Devices::Cuda, int >" ) );
+ EXPECT_EQ( mtrxCudaInt.getType(), TNL::String( "Matrices::Dense< int, Devices::Cuda, int >" ) );
}
template< typename Matrix >
@@ -1397,7 +1394,6 @@ TYPED_TEST( MatrixTest, printTest )
TEST( DenseMatrixTest, Dense_getMatrixProductTest_Host )
{
-// test_GetMatrixProduct< Dense_host_int >();
bool testRan = false;
EXPECT_TRUE( testRan );
std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
@@ -1414,7 +1410,6 @@ TEST( DenseMatrixTest, Dense_getMatrixProductTest_Host )
#ifdef HAVE_CUDA
TEST( DenseMatrixTest, Dense_getMatrixProductTest_Cuda )
{
-// test_GetMatrixProduct< Dense_cuda_int >();
bool testRan = false;
EXPECT_TRUE( testRan );
std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
diff --git a/src/UnitTests/Matrices/SparseMatrixTest.hpp b/src/UnitTests/Matrices/SparseMatrixTest.hpp
index 8beaa5b296985c41107c7a881eea81d832f04a0c..ef5b28d240a65c5e26eb987c42b76688c59a8d87 100644
--- a/src/UnitTests/Matrices/SparseMatrixTest.hpp
+++ b/src/UnitTests/Matrices/SparseMatrixTest.hpp
@@ -28,12 +28,6 @@ void host_test_GetType()
EXPECT_TRUE( testRan );
std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
std::cerr << "This test has not been implemented properly yet.\n" << std::endl;
-
-// MatrixHostFloat mtrxHostFloat;
-// MatrixHostInt mtrxHostInt;
-//
-// EXPECT_EQ( mtrxHostFloat.getType(), TNL::String( "Matrices::CSR< float, Devices::Host >" ) );
-// EXPECT_EQ( mtrxHostInt.getType(), TNL::String( "Matrices::CSR< int, Devices::Host >" ) );
}
template< typename MatrixCudaFloat, typename MatrixCudaInt >
@@ -42,13 +36,7 @@ void cuda_test_GetType()
bool testRan = false;
EXPECT_TRUE( testRan );
std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
- std::cerr << "This test has not been implemented properly yet.\n" << std::endl;
-
-// MatrixCudaFloat mtrxCudaFloat;
-// MatrixCudaInt mtrxCudaInt;
-//
-// EXPECT_EQ( mtrxCudaFloat.getType(), TNL::String( "Matrices::CSR< float, Devices::Cuda >" ) );
-// EXPECT_EQ( mtrxCudaInt.getType(), TNL::String( "Matrices::CSR< int, Devices::Cuda >" ) );
+ std::cerr << "This test has not been implemented properly yet.\n" << std::endl;
}
template< typename Matrix >
@@ -224,7 +212,6 @@ void test_SetElement()
typename Matrix::CompressedRowLengthsVector rowLengths;
rowLengths.setSize( rows );
-// rowLengths.setValue( 8 );
rowLengths.setElement( 0, 4 );
rowLengths.setElement( 1, 3 );
rowLengths.setElement( 2, 8 );
@@ -614,7 +601,6 @@ void test_VectorProduct()
using IndexType = typename Matrix::IndexType;
using VectorType = TNL::Containers::Vector< RealType, DeviceType, IndexType >;
-// Matrix totalLoad (AdEll) = 1
/*
* Sets up the following 4x4 sparse matrix:
*
@@ -668,7 +654,6 @@ void test_VectorProduct()
EXPECT_EQ( outVector_1.getElement( 3 ), 10 );
-// Matrix totalLoad (AdEll) = 2
/*
* Sets up the following 4x4 sparse matrix:
*
@@ -723,7 +708,6 @@ void test_VectorProduct()
EXPECT_EQ( outVector_2.getElement( 3 ), 16 );
-// Matrix totalLoad (AdEll) = 3
/*
* Sets up the following 4x4 sparse matrix:
*
@@ -777,7 +761,6 @@ void test_VectorProduct()
EXPECT_EQ( outVector_3.getElement( 3 ), 66 );
-// Matrix totalLoad (AdEll) = 4
/*
* Sets up the following 8x8 sparse matrix:
*
@@ -856,7 +839,6 @@ void test_VectorProduct()
EXPECT_EQ( outVector_4.getElement( 7 ), 330 );
-// Matrix totalLoad (AdEll) = 5
/*
* Sets up the following 8x8 sparse matrix:
*
@@ -942,9 +924,6 @@ void test_VectorProduct()
EXPECT_EQ( outVector_5.getElement( 5 ), 224 );
EXPECT_EQ( outVector_5.getElement( 6 ), 352 );
EXPECT_EQ( outVector_5.getElement( 7 ), 520 );
-
-
- // ONE MORE TEST HERE FOR 16X16
}
template< typename Matrix >
@@ -1054,19 +1033,6 @@ void test_OperatorEquals()
* | 22 23 24 25 26 27 28 1 | 8
* \ 29 30 31 32 33 34 35 36 / 8
*/
-
- /* Sorted BiELL:
- *
- *
- * / 22 23 24 25 26 27 28 1 \
- * | 29 30 31 32 33 34 35 36 |
- * | 16 17 18 19 20 21 1 |
- * | 1 2 3 4 5 1 |
- * | 11 12 13 14 1 |
- * | 8 9 10 1 |
- * | 6 7 1 |
- * \ 15 1 /
- */
const IndexType m_rows = 8;
const IndexType m_cols = 8;
@@ -1453,7 +1419,6 @@ void test_Print()
std::cout.rdbuf(old_buf); //reset
- //printed << printed.str() << std::endl;
couted << "Row: 0 -> Col:0->1 Col:1->2 Col:2->3\t\n"
"Row: 1 -> Col:3->4\t\n"
"Row: 2 -> Col:0->5 Col:1->6 Col:2->7\t\n"
diff --git a/src/UnitTests/Matrices/SparseMatrixTest_AdEllpack.h b/src/UnitTests/Matrices/SparseMatrixTest_AdEllpack.h
index 33a2403bc6563dff52f27da860b3dcdd71bb81a9..aac3a41a8fc895aa0c48136ad6542c69c45cb839 100644
--- a/src/UnitTests/Matrices/SparseMatrixTest_AdEllpack.h
+++ b/src/UnitTests/Matrices/SparseMatrixTest_AdEllpack.h
@@ -78,7 +78,6 @@ TYPED_TEST( AdEllpackMatrixTest, resetTest )
test_Reset< AdEllpackMatrixType >();
}
-// SUPPOSEDLY WORKING - localLoad, offset and rowOffset are seemingly random numbers in the head and tail of WarpList.
TYPED_TEST( AdEllpackMatrixTest, setElementTest )
{
using AdEllpackMatrixType = typename TestFixture::AdEllpackMatrixType;
@@ -86,7 +85,6 @@ TYPED_TEST( AdEllpackMatrixTest, setElementTest )
test_SetElement< AdEllpackMatrixType >();
}
-// SUPPOSEDLY WORKING - localLoad, offset and rowOffset are seemingly random numbers in the head and tail of WarpList.
TYPED_TEST( AdEllpackMatrixTest, addElementTest )
{
using AdEllpackMatrixType = typename TestFixture::AdEllpackMatrixType;
@@ -94,7 +92,6 @@ TYPED_TEST( AdEllpackMatrixTest, addElementTest )
test_AddElement< AdEllpackMatrixType >();
}
-// SUPPOSEDLY WORKING - Tests take longer than expected. setElement takes 13ms, compared to SlicedEllpack's 2ms.
TYPED_TEST( AdEllpackMatrixTest, setRowTest )
{
using AdEllpackMatrixType = typename TestFixture::AdEllpackMatrixType;
@@ -102,7 +99,6 @@ TYPED_TEST( AdEllpackMatrixTest, setRowTest )
test_SetRow< AdEllpackMatrixType >();
}
-// WORKS FOR MATRICES up to 99x99, The rest have different results.
TYPED_TEST( AdEllpackMatrixTest, vectorProductTest )
{
using AdEllpackMatrixType = typename TestFixture::AdEllpackMatrixType;
@@ -110,7 +106,6 @@ TYPED_TEST( AdEllpackMatrixTest, vectorProductTest )
test_VectorProduct< AdEllpackMatrixType >();
}
-// TODO test
TYPED_TEST( AdEllpackMatrixTest, operatorEqualsTest )
{
using AdEllpackMatrixType = typename TestFixture::AdEllpackMatrixType;
diff --git a/src/UnitTests/Matrices/SparseMatrixTest_BiEllpack.h b/src/UnitTests/Matrices/SparseMatrixTest_BiEllpack.h
index e8b4412559af2fff19c8c95330f7a0cfb2ff6d80..c55eb101f009e3b39ab3bf90d3b809d0d608d0ec 100644
--- a/src/UnitTests/Matrices/SparseMatrixTest_BiEllpack.h
+++ b/src/UnitTests/Matrices/SparseMatrixTest_BiEllpack.h
@@ -57,7 +57,6 @@ using BiEllpackMatrixTypes = ::testing::Types
TYPED_TEST_SUITE( BiEllpackMatrixTest, BiEllpackMatrixTypes);
-// WORKING
TYPED_TEST( BiEllpackMatrixTest, setDimensionsTest )
{
using BiEllpackMatrixType = typename TestFixture::BiEllpackMatrixType;
diff --git a/src/UnitTests/Matrices/SparseMatrixTest_ChunkedEllpack.h b/src/UnitTests/Matrices/SparseMatrixTest_ChunkedEllpack.h
index 91bd7fa9790e03e6e2a20918057c2e6c8a10dd48..5ef97a1df004095479864ab9d4ddc89fdbb54a4d 100644
--- a/src/UnitTests/Matrices/SparseMatrixTest_ChunkedEllpack.h
+++ b/src/UnitTests/Matrices/SparseMatrixTest_ChunkedEllpack.h
@@ -24,10 +24,6 @@ protected:
using ChunkedEllpackMatrixType = Matrix;
};
-// columnIndexes of ChunkedEllpack appear to be broken, when printed, it prints out a bunch of 4s.
-// rowPointers have interesting elements? 0 18 36 42 54 72 96 126 162 204 256 when rows = 10, cols = 11; rowLengths = 3 3 1 2 3 4 5 6 7 8
-// and 0 52 103 154 205 256 when rows = 5, cols = 4; rowLengths = 3 3 3 3 3
-
// types for which MatrixTest is instantiated
using ChEllpackMatrixTypes = ::testing::Types