## Uses of Class
org.apache.sysds.runtime.instructions.gpu.context.GPUContext

- Packages that use GPUContext
  Package | Description
  --- | ---
  org.apache.sysds.runtime.controlprogram.caching |
  org.apache.sysds.runtime.controlprogram.context |
  org.apache.sysds.runtime.instructions.gpu.context |
  org.apache.sysds.runtime.matrix.data |

- - ### Uses of GPUContext in org.apache.sysds.runtime.controlprogram.caching

    Methods in org.apache.sysds.runtime.controlprogram.caching with parameters of type GPUContext
    Modifier and Type | Method and Description
    --- | ---
    `GPUObject` | CacheableData.`getGPUObject(GPUContext gCtx)`
    `void` | CacheableData.`removeGPUObject(GPUContext gCtx)`
    `void` | CacheableData.`setGPUObject(GPUContext gCtx, GPUObject gObj)`

  - ### Uses of GPUContext in org.apache.sysds.runtime.controlprogram.context

    Methods in org.apache.sysds.runtime.controlprogram.context that return GPUContext
    Modifier and Type | Method and Description
    --- | ---
    `GPUContext` | ExecutionContext.`getGPUContext(int index)` Get the i-th GPUContext

    Methods in org.apache.sysds.runtime.controlprogram.context that return types with arguments of type GPUContext
    Modifier and Type | Method and Description
    --- | ---
    `List<GPUContext>` | ExecutionContext.`getGPUContexts()` Gets the list of GPUContexts

    Method parameters in org.apache.sysds.runtime.controlprogram.context with type arguments of type GPUContext
    Modifier and Type | Method and Description
    --- | ---
    `void` | ExecutionContext.`setGPUContexts(List<GPUContext> gpuContexts)` Sets the list of GPUContexts

  - ### Uses of GPUContext in org.apache.sysds.runtime.instructions.gpu.context

    Methods in org.apache.sysds.runtime.instructions.gpu.context that return types with arguments of type GPUContext
    Modifier and Type | Method and Description
    --- | ---
    `static List<GPUContext>` | GPUContextPool.`reserveAllGPUContexts()` Reserves and gets an initialized list of GPUContexts

    Methods in org.apache.sysds.runtime.instructions.gpu.context with parameters of type GPUContext
    Modifier and Type | Method and Description
    --- | ---
    `static CSRPointer` | CSRPointer.`allocateEmpty(GPUContext gCtx, long nnz2, long rows)` Factory method to allocate an empty CSR Sparse matrix on the GPU
    `static CSRPointer` | CSRPointer.`allocateForDgeam(GPUContext gCtx, jcuda.jcusparse.cusparseHandle handle, CSRPointer A, CSRPointer B, int m, int n)` Estimates the number of non zero elements from the results of a sparse cusparseDgeam operation C = a op(A) + b op(B)
    `static CSRPointer` | CSRPointer.`allocateForMatrixMultiply(GPUContext gCtx, jcuda.jcusparse.cusparseHandle handle, CSRPointer A, int transA, CSRPointer B, int transB, int m, int n, int k)` Estimates the number of non-zero elements from the result of a sparse matrix multiplication C = A * B and returns the `CSRPointer` to C with the appropriate GPU memory.
    `static CSRPointer` | GPUObject.`columnMajorDenseToRowMajorSparse(GPUContext gCtx, jcuda.jcusparse.cusparseHandle cusparseHandle, jcuda.Pointer densePtr, int rows, int cols)` Convenience method to convert a CSR matrix to a dense matrix on the GPU Since the allocated matrix is temporary, bookkeeping is not updated.
    `static void` | CSRPointer.`copyToDevice(GPUContext gCtx, CSRPointer dest, int rows, long nnz, int[] rowPtr, int[] colInd, double[] values)` Static method to copy a CSR sparse matrix from Host to Device
    `static jcuda.Pointer` | GPUObject.`transpose(GPUContext gCtx, jcuda.Pointer densePtr, int m, int n, int lda, int ldc)` Transposes a dense matrix on the GPU by calling the cublasDgeam operation

    Constructors in org.apache.sysds.runtime.instructions.gpu.context with parameters of type GPUContext
    Constructor and Description
    `GPUMemoryManager(GPUContext gpuCtx)`
    `GPUObject(GPUContext gCtx, GPUObject that, MatrixObject mat)`

  - ### Uses of GPUContext in org.apache.sysds.runtime.matrix.data

    Methods in org.apache.sysds.runtime.matrix.data with parameters of type GPUContext
    Modifier and Type | Method and Description
    --- | ---
    `static void` | LibMatrixCUDA.`abs(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "abs" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`acos(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "acos" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`asin(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "asin" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`atan(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "atan" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`axpy(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, MatrixObject in2, String outputName, double constant)` Performs daxpy operation
    `static void` | LibMatrixCuDNN.`batchNormalizationBackward(GPUContext gCtx, String instName, MatrixObject image, MatrixObject dout, MatrixObject scale, MatrixObject dX, MatrixObject dScale, MatrixObject dBias, double epsilon, MatrixObject resultSaveMean, MatrixObject resultSaveInvVariance)` This method computes the backpropagation errors for image, scale and bias of batch normalization layer
    `static void` | LibMatrixCuDNN.`batchNormalizationForwardInference(GPUContext gCtx, String instName, MatrixObject image, MatrixObject scale, MatrixObject bias, MatrixObject runningMean, MatrixObject runningVar, MatrixObject ret, double epsilon)` Performs the forward BatchNormalization layer computation for inference
    `static void` | LibMatrixCuDNN.`batchNormalizationForwardTraining(GPUContext gCtx, String instName, MatrixObject image, MatrixObject scale, MatrixObject bias, MatrixObject runningMean, MatrixObject runningVar, MatrixObject ret, MatrixObject retRunningMean, MatrixObject retRunningVar, double epsilon, double exponentialAverageFactor, MatrixObject resultSaveMean, MatrixObject resultSaveInvVariance)` Performs the forward BatchNormalization layer computation for training
    `static void` | LibMatrixCUDA.`biasAdd(GPUContext gCtx, String instName, MatrixObject input, MatrixObject bias, MatrixObject outputBlock)` Performs the operation corresponding to the DML script: ones = matrix(1, rows=1, cols=Hout*Wout) output = input + matrix(bias %*% ones, rows=1, cols=F*Hout*Wout) This operation is often followed by conv2d and hence we have introduced bias_add(input, bias) built-in function
    `static void` | LibMatrixCUDA.`biasMultiply(GPUContext gCtx, String instName, MatrixObject input, MatrixObject bias, MatrixObject outputBlock)` Performs the operation corresponding to the DML script: ones = matrix(1, rows=1, cols=Hout*Wout) output = input * matrix(bias %*% ones, rows=1, cols=F*Hout*Wout) This operation is often followed by conv2d and hence we have introduced bias_add(input, bias) built-in function
    `static void` | LibMatrixCUDA.`cbind(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, MatrixObject in2, String outputName)`
    `static void` | LibMatrixCUDA.`ceil(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "ceil" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`channelSums(GPUContext gCtx, String instName, MatrixObject input, MatrixObject outputBlock, long C, long HW)` Perform channel_sums operations: out = rowSums(matrix(colSums(A), rows=C, cols=HW))
    `static int` | LibMatrixCUDA.`computeNNZ(GPUContext gCtx, jcuda.Pointer densePtr, int length)` Utility to compute number of non-zeroes on the GPU
    `static void` | LibMatrixCuDNN.`conv2d(GPUContext gCtx, String instName, MatrixObject image, MatrixObject filter, MatrixObject outputBlock, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, double intermediateMemoryBudget)` Performs a 2D convolution
    `static void` | LibMatrixCuDNN.`conv2dBackwardData(GPUContext gCtx, String instName, MatrixObject filter, MatrixObject dout, MatrixObject output, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, double intermediateMemoryBudget)` This method computes the backpropogation errors for previous layer of convolution operation
    `static void` | LibMatrixCuDNN.`conv2dBackwardFilter(GPUContext gCtx, String instName, MatrixObject image, MatrixObject dout, MatrixObject outputBlock, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, double intermediateMemoryBudget)` This method computes the backpropogation errors for filter of convolution operation
    `static void` | LibMatrixCuDNN.`conv2dBiasAdd(GPUContext gCtx, String instName, MatrixObject image, MatrixObject bias, MatrixObject filter, MatrixObject output, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, double intermediateMemoryBudget)` Does a 2D convolution followed by a bias_add
    `static void` | LibMatrixCUDA.`cos(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "cos" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`cosh(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "cosh" operation on a matrix on the GPU
    `static LibMatrixCuDNNConvolutionAlgorithm` | LibMatrixCuDNNConvolutionAlgorithm.`cudnnGetConvolutionBackwardDataAlgorithm(GPUContext gCtx, String instName, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, long workspaceLimit)` Factory method to get the algorithm wrapper for convolution backward data
    `static LibMatrixCuDNNConvolutionAlgorithm` | LibMatrixCuDNNConvolutionAlgorithm.`cudnnGetConvolutionBackwardFilterAlgorithm(GPUContext gCtx, String instName, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, long workspaceLimit)` Factory method to get the algorithm wrapper for convolution backward filter
    `static LibMatrixCuDNNConvolutionAlgorithm` | LibMatrixCuDNNConvolutionAlgorithm.`cudnnGetConvolutionForwardAlgorithm(GPUContext gCtx, String instName, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, long workspaceLimit)` Factory method to get the algorithm wrapper for convolution forward
    `static LibMatrixCuDNNPoolingDescriptors` | LibMatrixCuDNNPoolingDescriptors.`cudnnPoolingBackwardDescriptors(GPUContext gCtx, String instName, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, LibMatrixDNN.PoolingType poolingType)` Get descriptors for maxpooling backward operation
    `static LibMatrixCuDNNPoolingDescriptors` | LibMatrixCuDNNPoolingDescriptors.`cudnnPoolingDescriptors(GPUContext gCtx, String instName, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, LibMatrixDNN.PoolingType poolingType)` Get descriptors for maxpooling operation
    `static void` | LibMatrixCUDA.`cumulativeScan(ExecutionContext ec, GPUContext gCtx, String instName, String kernelFunction, MatrixObject in, String outputName)` Cumulative scan
    `static void` | LibMatrixCUDA.`cumulativeSumProduct(ExecutionContext ec, GPUContext gCtx, String instName, String kernelFunction, MatrixObject in, String outputName)` Cumulative sum-product kernel cascade invokation
    `static void` | LibMatrixCUDA.`denseTranspose(ExecutionContext ec, GPUContext gCtx, String instName, jcuda.Pointer A, jcuda.Pointer C, long numRowsA, long numColsA)` Computes C = t(A)
    `void` | CudaSupportFunctions.`deviceToHost(GPUContext gCtx, jcuda.Pointer src, double[] dest, String instName, boolean isEviction)`
    `void` | DoublePrecisionCudaSupportFunctions.`deviceToHost(GPUContext gCtx, jcuda.Pointer src, double[] dest, String instName, boolean isEviction)`
    `void` | SinglePrecisionCudaSupportFunctions.`deviceToHost(GPUContext gCtx, jcuda.Pointer src, double[] dest, String instName, boolean isEviction)`
    `static jcuda.Pointer` | LibMatrixCUDA.`double2float(GPUContext gCtx, jcuda.Pointer A, jcuda.Pointer ret, int numElems)`
    `static void` | LibMatrixCUDA.`exp(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "exp" operation on a matrix on the GPU
    `static jcuda.Pointer` | LibMatrixCUDA.`float2double(GPUContext gCtx, jcuda.Pointer A, jcuda.Pointer ret, int numElems)`
    `static void` | LibMatrixCUDA.`floor(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "floor" operation on a matrix on the GPU
    `static JCudaKernels` | LibMatrixCUDA.`getCudaKernels(GPUContext gCtx)`
    `static jcuda.Pointer` | LibMatrixCUDA.`getDensePointer(GPUContext gCtx, MatrixObject input, String instName)` Convenience method to get jcudaDenseMatrixPtr.
    `static jcuda.Pointer` | LibMatrixCuDNN.`getDensePointerForCuDNN(GPUContext gCtx, MatrixObject image, String instName, int numRows, int numCols)` Convenience method to get jcudaDenseMatrixPtr.
    `static long` | LibMatrixCUDA.`getNnz(GPUContext gCtx, String instName, MatrixObject mo, boolean recomputeDenseNNZ)` Note: if the matrix is in dense format, it explicitly re-computes the number of nonzeros.
    `void` | CudaSupportFunctions.`hostToDevice(GPUContext gCtx, double[] src, jcuda.Pointer dest, String instName)`
    `void` | DoublePrecisionCudaSupportFunctions.`hostToDevice(GPUContext gCtx, double[] src, jcuda.Pointer dest, String instName)`
    `void` | SinglePrecisionCudaSupportFunctions.`hostToDevice(GPUContext gCtx, double[] src, jcuda.Pointer dest, String instName)`
    `static boolean` | LibMatrixCUDA.`isInSparseFormat(GPUContext gCtx, MatrixObject mo)`
    `static void` | LibMatrixCUDA.`log(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "log" operation on a matrix on the GPU
    `static void` | LibMatrixCuDNN.`lstm(ExecutionContext ec, GPUContext gCtx, String instName, jcuda.Pointer X, jcuda.Pointer wPointer, jcuda.Pointer out0, jcuda.Pointer c0, boolean return_sequences, String outputName, String cyName, int N, int M, int D, int T)` Computes the forward pass for an LSTM layer with M neurons.
    `static void` | LibMatrixCuDNN.`lstmBackward(ExecutionContext ec, GPUContext gCtx, String instName, jcuda.Pointer x, jcuda.Pointer hx, jcuda.Pointer cx, jcuda.Pointer wPointer, String doutName, String dcyName, String dxName, String dwName, String dbName, String dhxName, String dcxName, boolean return_sequences, int N, int M, int D, int T)`
    `static MatrixObject` | LibMatrixCuMatMult.`matmult(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject left, MatrixObject right, String outputName, boolean isLeftTransposed, boolean isRightTransposed)` Matrix multiply on GPU Examines sparsity and shapes and routes call to appropriate method from cuBLAS or cuSparse C = op(A) x op(B) The user is expected to call ec.releaseMatrixOutputForGPUInstruction(outputName);
    `static void` | LibMatrixCUDA.`matmultTSMM(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject left, String outputName, boolean isLeftTransposed)` Performs tsmm, A %*% A' or A' %*% A, on GPU by exploiting cublasDsyrk(...)
    `static void` | LibMatrixCUDA.`matrixMatrixArithmetic(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, MatrixObject in2, String outputName, boolean isLeftTransposed, boolean isRightTransposed, BinaryOperator op)` Performs elementwise arithmetic operation specified by op of two input matrices in1 and in2
    `static void` | LibMatrixCUDA.`matrixMatrixRelational(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, MatrixObject in2, String outputName, BinaryOperator op)` Performs elementwise operation relational specified by op of two input matrices in1 and in2
    `static void` | LibMatrixCUDA.`matrixScalarArithmetic(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in, String outputName, boolean isInputTransposed, ScalarOperator op)` Entry point to perform elementwise matrix-scalar arithmetic operation specified by op
    `static void` | LibMatrixCUDA.`matrixScalarOp(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in, String outputName, boolean isInputTransposed, ScalarOperator op)` Utility to do matrix-scalar operation kernel
    `static void` | LibMatrixCUDA.`matrixScalarRelational(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in, String outputName, ScalarOperator op)` Entry point to perform elementwise matrix-scalar relational operation specified by op
    `static void` | LibMatrixCuDNN.`pooling(GPUContext gCtx, String instName, MatrixObject image, MatrixObject outputBlock, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, LibMatrixDNN.PoolingType poolingType, double intermediateMemoryBudget)` performs maxpooling on GPU by exploiting cudnnPoolingForward(...)
    `static void` | LibMatrixCuDNN.`poolingBackward(GPUContext gCtx, String instName, MatrixObject image, MatrixObject dout, MatrixObject maxpoolOutput, MatrixObject outputBlock, int N, int C, int H, int W, int K, int R, int S, int pad_h, int pad_w, int stride_h, int stride_w, int P, int Q, LibMatrixDNN.PoolingType poolingType, double intermediateMemoryBudget)` Performs maxpoolingBackward on GPU by exploiting cudnnPoolingBackward(...) This method computes the backpropogation errors for previous layer of maxpooling operation
    `static void` | LibMatrixCUDA.`rbind(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, MatrixObject in2, String outputName)`
    `static void` | LibMatrixCuDNN.`relu(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in, String outputName)` Performs the relu operation on the GPU.
    `static void` | LibMatrixCUDA.`reluBackward(GPUContext gCtx, String instName, MatrixObject input, MatrixObject dout, MatrixObject outputBlock)` This method computes the backpropagation errors for previous layer of relu operation
    `static void` | LibMatrixCUDA.`round(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "round" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`sigmoid(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "sigmoid" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`sign(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "sign" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`sin(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "sin" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`sinh(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "sinh" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`sliceOperations(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, IndexRange ixrange, String outputName)` Method to perform rightIndex operation for a given lower and upper bounds in row and column dimensions.
    `static void` | LibMatrixCuDNN.`softmax(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "softmax" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`solve(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, MatrixObject in2, String outputName)` Implements the "solve" function for systemds Ax = B (A is of size m*n, B is of size m*1, x is of size n*1)
    `static void` | LibMatrixCUDA.`sqrt(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "sqrt" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`tan(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "tan" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`tanh(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String outputName)` Performs an "tanh" operation on a matrix on the GPU
    `static void` | LibMatrixCUDA.`transpose(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in, String outputName)` Transposes the input matrix using cublasDgeam
    `static void` | LibMatrixCUDA.`unaryAggregate(ExecutionContext ec, GPUContext gCtx, String instName, MatrixObject in1, String output, AggregateUnaryOperator op)` Entry point to perform Unary aggregate operations on the GPU.

    Constructors in org.apache.sysds.runtime.matrix.data with parameters of type GPUContext
    Constructor and Description
    `LibMatrixCuDNNInputRowFetcher(GPUContext gCtx, String instName, MatrixObject image)` Initialize the input fetcher
    `LibMatrixCuDNNRnnAlgorithm(ExecutionContext ec, GPUContext gCtx, String instName, String rnnMode, int N, int T, int M, int D, boolean isTraining, jcuda.Pointer w)`

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