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If the given result is different to cublasStatus.CUBLAS_STATUS_SUCCESS and
exceptions have been enab

checkResult

Obtain the current CUBLAS status by calling cublasGetErrorNative, and store the
result as the lastRe

checkResultBLAS

cublasAllocNative

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cublasCcopyNative

cublasCdotcNative

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cublasCgbmvNative

cublasCgemmNative

cublasCgemvNative

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Gson

Reactive rest calls using spring rest template

A generic Abstract Window Toolkit(AWT) container object is a component that can
contain other AWT co

java.awt.Container

A readable source of bytes.Most clients will use input streams that read data
from the file system (

java.io.InputStream

A specific moment in time, with millisecond precision. Values typically come
from System#currentTime

java.util.Date

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of regular expressio

java.util.Scanner

An ExecutorService that executes each submitted task using one of possibly
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Defines methods that all servlets must implement.A servlet is a small Java
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onCreateOptionsMenu

scheduleAtFixedRate

Schedules the specified task for repeated fixed-rate execution, beginning after
the specified delay.

/**
     * <pre>
     * void
     * cublasSgemm (char transa, char transb, int m, int n, int k, float alpha,
     *              const float *A, int lda, const float *B, int ldb, float beta,
     *              float *C, int ldc)
     *
     * computes the product of matrix A and matrix B, multiplies the result
     * by a scalar alpha, and adds the sum to the product of matrix C and
     * scalar beta. sgemm() performs one of the matrix-matrix operations:
     *
     *     C = alpha * op(A) * op(B) + beta * C,
     *
     * where op(X) is one of
     *
     *     op(X) = X   or   op(X) = transpose(X)
     *
     * alpha and beta are single precision scalars, and A, B and C are
     * matrices consisting of single precision elements, with op(A) an m x k
     * matrix, op(B) a k x n matrix, and C an m x n matrix. Matrices A, B,
     * and C are stored in column major format, and lda, ldb, and ldc are
     * the leading dimensions of the two-dimensional arrays containing A,
     * B, and C.
     *
     * Input
     * -----
     * transa specifies op(A). If transa = 'n' or 'N', op(A) = A. If
     *        transa = 't', 'T', 'c', or 'C', op(A) = transpose(A)
     * transb specifies op(B). If transb = 'n' or 'N', op(B) = B. If
     *        transb = 't', 'T', 'c', or 'C', op(B) = transpose(B)
     * m      number of rows of matrix op(A) and rows of matrix C
     * n      number of columns of matrix op(B) and number of columns of C
     * k      number of columns of matrix op(A) and number of rows of op(B)
     * alpha  single precision scalar multiplier applied to op(A)op(B)
     * A      single precision array of dimensions (lda, k) if transa =
     *        'n' or 'N'), and of dimensions (lda, m) otherwise. When transa =
     *        'N' or 'n' then lda must be at least  max( 1, m ), otherwise lda
     *        must be at least max(1, k).
     * lda    leading dimension of two-dimensional array used to store matrix A
     * B      single precision array of dimensions  (ldb, n) if transb =
     *        'n' or 'N'), and of dimensions (ldb, k) otherwise. When transb =
     *        'N' or 'n' then ldb must be at least  max (1, k), otherwise ldb
     *        must be at least max (1, n).
     * ldb    leading dimension of two-dimensional array used to store matrix B
     * beta   single precision scalar multiplier applied to C. If 0, C does
     *        not have to be a valid input
     * C      single precision array of dimensions (ldc, n). ldc must be at
     *        least max (1, m).
     * ldc    leading dimension of two-dimensional array used to store matrix C
     *
     * Output
     * ------
     * C      updated based on C = alpha * op(A)*op(B) + beta * C
     *
     * Reference: http://www.netlib.org/blas/sgemm.f
     *
     * Error status for this function can be retrieved via cublasGetError().
     *
     * Error Status
     * ------------
     * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized
     * CUBLAS_STATUS_INVALID_VALUE    if any of m, n, or k are < 0
     * CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
     * </pre>
     */

/**
   * <pre>
   * void 
   * cublasSgemm (char transa, char transb, int m, int n, int k, float alpha, 
   *              const float *A, int lda, const float *B, int ldb, float beta, 
   *              float *C, int ldc)
   *
   * computes the product of matrix A and matrix B, multiplies the result 
   * by a scalar alpha, and adds the sum to the product of matrix C and
   * scalar beta. sgemm() performs one of the matrix-matrix operations:
   *
   *     C = alpha * op(A) * op(B) + beta * C,
   *
   * where op(X) is one of
   *
   *     op(X) = X   or   op(X) = transpose(X)
   *
   * alpha and beta are single precision scalars, and A, B and C are 
   * matrices consisting of single precision elements, with op(A) an m x k 
   * matrix, op(B) a k x n matrix, and C an m x n matrix. Matrices A, B, 
   * and C are stored in column major format, and lda, ldb, and ldc are
   * the leading dimensions of the two-dimensional arrays containing A, 
   * B, and C.
   *
   * Input
   * -----
   * transa specifies op(A). If transa = 'n' or 'N', op(A) = A. If 
   *        transa = 't', 'T', 'c', or 'C', op(A) = transpose(A)
   * transb specifies op(B). If transb = 'n' or 'N', op(B) = B. If 
   *        transb = 't', 'T', 'c', or 'C', op(B) = transpose(B)
   * m      number of rows of matrix op(A) and rows of matrix C
   * n      number of columns of matrix op(B) and number of columns of C
   * k      number of columns of matrix op(A) and number of rows of op(B) 
   * alpha  single precision scalar multiplier applied to op(A)op(B)
   * A      single precision array of dimensions (lda, k) if transa = 
   *        'n' or 'N'), and of dimensions (lda, m) otherwise. When transa =
   *        'N' or 'n' then lda must be at least  max( 1, m ), otherwise lda
   *        must be at least max(1, k).
   * lda    leading dimension of two-dimensional array used to store matrix A
   * B      single precision array of dimensions  (ldb, n) if transb =
   *        'n' or 'N'), and of dimensions (ldb, k) otherwise. When transb =
   *        'N' or 'n' then ldb must be at least  max (1, k), otherwise ldb
   *        must be at least max (1, n).
   * ldb    leading dimension of two-dimensional array used to store matrix B
   * beta   single precision scalar multiplier applied to C. If 0, C does
   *        not have to be a valid input
   * C      single precision array of dimensions (ldc, n). ldc must be at 
   *        least max (1, m).
   * ldc    leading dimension of two-dimensional array used to store matrix C
   *
   * Output
   * ------
   * C      updated based on C = alpha * op(A)*op(B) + beta * C
   *
   * Reference: http://www.netlib.org/blas/sgemm.f
   *
   * Error status for this function can be retrieved via cublasGetError().
   *
   * Error Status
   * ------------
   * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized
   * CUBLAS_STATUS_INVALID_VALUE    if any of m, n, or k are < 0
   * CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
   * </pre>
   */

How to use cublasSgemmNativemethodin jcuda.jcublas.JCublas

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How to use
cublasSgemmNative
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jcuda.jcublas.JCublas