Examples with GridExecutioner org.nd4j.linalg.api.ops.executioner.GridExecutioner used on opensource projects

Example 41 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project nd4j by deeplearning4j.

the class JcublasLapack method dgetrf.

@Override
public void dgetrf(int M, int N, INDArray A, INDArray IPIV, INDArray INFO) {
    INDArray a = A;
    if (Nd4j.dataType() != DataBuffer.Type.DOUBLE)
        log.warn("FLOAT getrf called in FLOAT environment");
    if (A.ordering() == 'c')
        a = A.dup('f');
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    // Get context for current thread
    CudaContext ctx = (CudaContext) allocator.getDeviceContext().getContext();
    // setup the solver handles for cuSolver calls
    cusolverDnHandle_t handle = ctx.getSolverHandle();
    cusolverDnContext solverDn = new cusolverDnContext(handle);
    // synchronized on the solver
    synchronized (handle) {
        int result = cusolverDnSetStream(new cusolverDnContext(handle), new CUstream_st(ctx.getOldStream()));
        if (result != 0)
            throw new BlasException("solverSetStream failed");
        // transfer the INDArray into GPU memory
        CublasPointer xAPointer = new CublasPointer(a, ctx);
        // this output - indicates how much memory we'll need for the real operation
        DataBuffer worksizeBuffer = Nd4j.getDataBufferFactory().createInt(1);
        int stat = cusolverDnDgetrf_bufferSize(solverDn, M, N, (DoublePointer) xAPointer.getDevicePointer(), M, // we intentionally use host pointer here
        (IntPointer) worksizeBuffer.addressPointer());
        if (stat != CUSOLVER_STATUS_SUCCESS) {
            throw new BlasException("cusolverDnDgetrf_bufferSize failed", stat);
        }
        int worksize = worksizeBuffer.getInt(0);
        // Now allocate memory for the workspace, the permutation matrix and a return code
        Pointer workspace = new Workspace(worksize * Nd4j.sizeOfDataType());
        // Do the actual LU decomp
        stat = cusolverDnDgetrf(solverDn, M, N, (DoublePointer) xAPointer.getDevicePointer(), M, new CudaPointer(workspace).asDoublePointer(), new CudaPointer(allocator.getPointer(IPIV, ctx)).asIntPointer(), new CudaPointer(allocator.getPointer(INFO, ctx)).asIntPointer());
        if (stat != CUSOLVER_STATUS_SUCCESS) {
            throw new BlasException("cusolverDnSgetrf failed", stat);
        }
    }
    allocator.registerAction(ctx, a);
    allocator.registerAction(ctx, INFO);
    allocator.registerAction(ctx, IPIV);
    if (a != A)
        A.assign(a);
}

Example 42 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project nd4j by deeplearning4j.

the class JcublasLapack method ssyev.

public int ssyev(char _jobz, char _uplo, int N, INDArray A, INDArray R) {
    int status = -1;
    int jobz = _jobz == 'V' ? CUSOLVER_EIG_MODE_VECTOR : CUSOLVER_EIG_MODE_NOVECTOR;
    int uplo = _uplo == 'L' ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;
    if (Nd4j.dataType() != DataBuffer.Type.FLOAT)
        log.warn("FLOAT ssyev called in DOUBLE environment");
    INDArray a = A;
    if (A.ordering() == 'c')
        a = A.dup('f');
    int M = A.rows();
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    // Get context for current thread
    CudaContext ctx = (CudaContext) allocator.getDeviceContext().getContext();
    // setup the solver handles for cuSolver calls
    cusolverDnHandle_t handle = ctx.getSolverHandle();
    cusolverDnContext solverDn = new cusolverDnContext(handle);
    // synchronized on the solver
    synchronized (handle) {
        status = cusolverDnSetStream(new cusolverDnContext(handle), new CUstream_st(ctx.getOldStream()));
        if (status == 0) {
            // transfer the INDArray into GPU memory
            CublasPointer xAPointer = new CublasPointer(a, ctx);
            CublasPointer xRPointer = new CublasPointer(R, ctx);
            // this output - indicates how much memory we'll need for the real operation
            DataBuffer worksizeBuffer = Nd4j.getDataBufferFactory().createInt(1);
            status = cusolverDnSsyevd_bufferSize(solverDn, jobz, uplo, M, (FloatPointer) xAPointer.getDevicePointer(), M, (FloatPointer) xRPointer.getDevicePointer(), (IntPointer) worksizeBuffer.addressPointer());
            if (status == CUSOLVER_STATUS_SUCCESS) {
                int worksize = worksizeBuffer.getInt(0);
                // allocate memory for the workspace, the non-converging row buffer and a return code
                Pointer workspace = new Workspace(worksize * Nd4j.sizeOfDataType());
                INDArray INFO = Nd4j.createArrayFromShapeBuffer(Nd4j.getDataBufferFactory().createInt(1), Nd4j.getShapeInfoProvider().createShapeInformation(new int[] { 1, 1 }));
                // Do the actual decomp
                status = cusolverDnSsyevd(solverDn, jobz, uplo, M, (FloatPointer) xAPointer.getDevicePointer(), M, (FloatPointer) xRPointer.getDevicePointer(), new CudaPointer(workspace).asFloatPointer(), worksize, new CudaPointer(allocator.getPointer(INFO, ctx)).asIntPointer());
                allocator.registerAction(ctx, INFO);
                if (status == 0)
                    status = INFO.getInt(0);
            }
        }
    }
    if (status == 0) {
        allocator.registerAction(ctx, R);
        allocator.registerAction(ctx, a);
        if (a != A)
            A.assign(a);
    }
    return status;
}

Example 43 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project nd4j by deeplearning4j.

the class JcublasLapack method sgetrf.

@Override
public void sgetrf(int M, int N, INDArray A, INDArray IPIV, INDArray INFO) {
    INDArray a = A;
    if (Nd4j.dataType() != DataBuffer.Type.FLOAT)
        log.warn("FLOAT getrf called in DOUBLE environment");
    if (A.ordering() == 'c')
        a = A.dup('f');
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    // Get context for current thread
    CudaContext ctx = (CudaContext) allocator.getDeviceContext().getContext();
    // setup the solver handles for cuSolver calls
    cusolverDnHandle_t handle = ctx.getSolverHandle();
    cusolverDnContext solverDn = new cusolverDnContext(handle);
    // synchronized on the solver
    synchronized (handle) {
        int result = cusolverDnSetStream(new cusolverDnContext(handle), new CUstream_st(ctx.getOldStream()));
        if (result != 0)
            throw new BlasException("solverSetStream failed");
        // transfer the INDArray into GPU memory
        CublasPointer xAPointer = new CublasPointer(a, ctx);
        // this output - indicates how much memory we'll need for the real operation
        DataBuffer worksizeBuffer = Nd4j.getDataBufferFactory().createInt(1);
        int stat = cusolverDnSgetrf_bufferSize(solverDn, M, N, (FloatPointer) xAPointer.getDevicePointer(), M, // we intentionally use host pointer here
        (IntPointer) worksizeBuffer.addressPointer());
        if (stat != CUSOLVER_STATUS_SUCCESS) {
            throw new BlasException("cusolverDnSgetrf_bufferSize failed", stat);
        }
        int worksize = worksizeBuffer.getInt(0);
        // Now allocate memory for the workspace, the permutation matrix and a return code
        Pointer workspace = new Workspace(worksize * Nd4j.sizeOfDataType());
        // Do the actual LU decomp
        stat = cusolverDnSgetrf(solverDn, M, N, (FloatPointer) xAPointer.getDevicePointer(), M, new CudaPointer(workspace).asFloatPointer(), new CudaPointer(allocator.getPointer(IPIV, ctx)).asIntPointer(), new CudaPointer(allocator.getPointer(INFO, ctx)).asIntPointer());
        if (stat != CUSOLVER_STATUS_SUCCESS) {
            throw new BlasException("cusolverDnSgetrf failed", stat);
        }
    }
    allocator.registerAction(ctx, a);
    allocator.registerAction(ctx, INFO);
    allocator.registerAction(ctx, IPIV);
    if (a != A)
        A.assign(a);
}