Uses of Class
jcuda.jcudpp.CUDPPHandle

Packages that use CUDPPHandle

jcuda.jcudpp

Contains the classes of JCudpp.

Uses of CUDPPHandle in jcuda.jcudpp

Methods in jcuda.jcudpp with parameters of type CUDPPHandle

static int	JCudpp.cudppCompact(CUDPPHandle planHandle, Pointer d_out, Pointer d_numValidElements, Pointer d_in, Pointer d_isValid, long numElements) Given an array d_in and an array of 1/0 flags in deviceValid, returns a compacted array in d_out of corresponding only the "valid" values from d_in. Takes as input an array of elements in GPU memory (d_in) and an equal-sized unsigned int array in GPU memory (deviceValid) that indicate which of those input elements are valid.
static int	JCudpp.cudppDestroyPlan(CUDPPHandle plan) Destroy a CUDPP Plan. Deletes the plan referred to by planHandle and all associated internal storage. Parameters:
static int	JCudpp.cudppDestroySparseMatrix(CUDPPHandle sparseMatrixHandle) Destroy a CUDPP Sparse Matrix Object. Deletes the sparse matrix data and plan referred to by sparseMatrixHandle and all associated internal storage. Parameters:
static int	JCudpp.cudppMultiScan(CUDPPHandle planHandle, Pointer d_out, Pointer d_in, long numElements, long numRows) Performs numRows parallel scan operations of numElements each on its input (d_in) and places the output in d_out, with the scan parameters set by config.
static int	JCudpp.cudppPlan(CUDPPHandle planHandle, CUDPPConfiguration config, long n, long rows, long rowPitch) Create a CUDPP plan. A plan is a data structure containing state and intermediate storage space that CUDPP uses to execute algorithms on data.
static int	JCudpp.cudppRand(CUDPPHandle planHandle, Pointer d_out, long numElements) Rand puts numElements random 32-bit elements into d_out. Outputs numElements random values to d_out.
static int	JCudpp.cudppRandSeed(CUDPPHandle planHandle, int seed) Sets the seed used for rand. The seed is crucial to any random number generator as it allows a sequence of random numbers to be replicated.
static int	JCudpp.cudppScan(CUDPPHandle planHandle, Pointer d_out, Pointer d_in, long numElements) Performs a scan operation of numElements on its input in GPU memory (d_in) and places the output in GPU memory (d_out), with the scan parameters specified in the plan pointed to by planHandle. The input to a scan operation is an input array, a binary associative operator (like + or max), and an identity element for that operator (+'s identity is 0).
static int	JCudpp.cudppSegmentedScan(CUDPPHandle planHandle, Pointer d_out, Pointer d_idata, Pointer d_iflags, long numElements) Performs a segmented scan operation of numElements on its input in GPU memory (d_idata) and places the output in GPU memory (d_out), with the scan parameters specified in the plan pointed to by planHandle. The input to a segmented scan operation is an input array of data, an input array of flags which demarcate segments, a binary associative operator (like + or max), and an identity element for that operator (+'s identity is 0).
static int	JCudpp.cudppSort(CUDPPHandle planHandle, Pointer d_keys, Pointer d_values, int keyBits, long numElements) Sorts key-value pairs or keys only. Takes as input an array of keys in GPU memory (d_keys) and an optional array of corresponding values, and outputs sorted arrays of keys and (optionally) values in place.
static int	JCudpp.cudppSparseMatrix(CUDPPHandle sparseMatrixHandle, CUDPPConfiguration config, long numNonZeroElements, long numRows, Pointer A, Pointer h_rowIndices, Pointer h_indices) Create a CUDPP Sparse Matrix Object. The sparse matrix plan is a data structure containing state and intermediate storage space that CUDPP uses to perform sparse matrix dense vector multiply.
static int	JCudpp.cudppSparseMatrixVectorMultiply(CUDPPHandle sparseMatrixHandle, Pointer d_y, Pointer d_x) Perform matrix-vector multiply y = A*x for arbitrary sparse matrix A and vector x. Given a matrix object handle (which has been initialized using cudppSparseMatrix()), This function multiplies the input vector d_x by the matrix referred to by sparseMatrixHandle, returning the result in d_y. Parameters: