# Package org.apache.sysds.runtime.matrix.data

Interface Summary
Converter<K1 extends org.apache.hadoop.io.Writable,V1 extends org.apache.hadoop.io.Writable,K2 extends org.apache.hadoop.io.Writable,V2 extends org.apache.hadoop.io.Writable>
CudaSupportFunctions: DESIGN DOCUMENTATION FOR SUPPORTING LOWER PRECISION: 1.
LibMatrixDNNRotate180.Rotate180Worker
MatrixBlockDataInput: Any data input that is intended to support fast deserialization / read of entire blocks should implement this interface.
MatrixBlockDataOutput: Any data output that is intended to support fast serialization / write of entire blocks should implement this interface.

Class Summary
BinaryBlockToTextCellConverter
CM_N_COVCell
CTableMap: Ctable map is an abstraction for the hashmap used for ctable's hash group-by because this structure is passed through various interfaces.
DnnParameters: This class is container that stores parameters required for executing following operations: conv2d, conv2d_backward_data, conv2d_backward_filter, maxpooling, maxpooling_backward
DoublePrecisionCudaSupportFunctions
FrameBlock
FrameBlock.ColumnMetadata
FrameBlock.FrameMapFunction
IJV: Helper class for external key/value exchange.
LibCommonsMath: Library for matrix operations that need invocation of Apache Commons Math library.
LibMatrixAgg: MB: Library for matrix aggregations including ak+, uak+ for all combinations of dense and sparse representations, and corrections.
LibMatrixBincell: Library for binary cellwise operations (incl arithmetic, relational, etc).
LibMatrixCountDistinct: This class contains various methods for counting the number of distinct values inside a MatrixBlock
LibMatrixCUDA: All CUDA kernels and library calls are redirected through this class
LibMatrixCuDNN: This class contains method that invoke CuDNN operations.
LibMatrixCuDNNConvolutionAlgorithm: This class is a wrapper that contain necessary data structures to invoke a cudnn convolution* functions (such as cudnnConvolutionForward, etc) It implements autocloseable to simplify the LibMatrixCuDNN code and also avoids potential memory leaks.
LibMatrixCuDNNInputRowFetcher: Performs a slice operation: out = in[(n+1):(n+1), 1:numColumns]
LibMatrixCuDNNPoolingDescriptors: This class is a wrapper that contain necessary data structures to invoke a cudnn convolution* functions (such as cudnnConvolutionForward, etc) It implements autocloseable to simplify the LibMatrixCuDNN code and also avoids potential memory leaks.
LibMatrixCuDNNRnnAlgorithm
LibMatrixCuMatMult
LibMatrixDatagen
LibMatrixDNN
LibMatrixDNNConv2d: This class contains the set of operators used for performing conv2d
LibMatrixDNNHelper
LibMatrixDNNIm2Col: This class contains the different implementation of im2col operation
LibMatrixDNNPooling: This class contains the set of operators used for performing pooling
LibMatrixDNNRelu: This class contains the different implementation of rotate180 operation
LibMatrixDNNRelu.ReluBackward: Performs the operation: (X gt 0) * dout
LibMatrixDNNRotate180: This class contains the different implementation of rotate180 operation
LibMatrixMult: MB: Library for matrix multiplications including MM, MV, VV for all combinations of dense, sparse, ultrasparse representations and special operations such as transpose-self matrix multiplication.
LibMatrixNative
LibMatrixOuterAgg: ACS: Purpose of this library is to make some of the unary outer aggregate operator more efficient.
LibMatrixReorg: MB: Library for selected matrix reorg operations including special cases and all combinations of dense and sparse representations.
LibMatrixTercell: Library for ternary cellwise operations.
MatrixBlock
MatrixBlock.SparsityEstimate
MatrixCell
MatrixIndexes: This represent the indexes to the blocks of the matrix.
MatrixValue
MatrixValue.CellIndex
OperationsOnMatrixValues
Pair<K,V>
RandomMatrixGenerator
SinglePrecisionCudaSupportFunctions
TextToBinaryCellConverter
TripleIndexes
TripleIndexes.Comparator
WeightedCell
WeightedPair

Enum Summary
LibMatrixBincell.BinaryAccessType
LibMatrixDNN.PoolingType
RandomMatrixGenerator.PDF: Types of Probability density functions