import numpy as np
from numba import cuda, float64, void
from numba.cuda.testing import unittest, CUDATestCase
from numba.core import config

# NOTE: CUDA kernel does not return any value

if config.ENABLE_CUDASIM:
    tpb = 4
else:
    tpb = 16
SM_SIZE = tpb, tpb


class TestCudaLaplace(CUDATestCase):
    def test_laplace_small(self):

        @cuda.jit(float64(float64, float64), device=True, inline=True)
        def get_max(a, b):
            if a > b:
                return a
            else:
                return b

        @cuda.jit(void(float64[:, :], float64[:, :], float64[:, :]))
        def jocabi_relax_core(A, Anew, error):
            err_sm = cuda.shared.array(SM_SIZE, dtype=float64)

            ty = cuda.threadIdx.x
            tx = cuda.threadIdx.y
            bx = cuda.blockIdx.x
            by = cuda.blockIdx.y

            n = A.shape[0]
            m = A.shape[1]

            i, j = cuda.grid(2)

            err_sm[ty, tx] = 0
            if j >= 1 and j < n - 1 and i >= 1 and i < m - 1:
                Anew[j, i] = 0.25 * ( A[j, i + 1] + A[j, i - 1]
                                      + A[j - 1, i] + A[j + 1, i])
                err_sm[ty, tx] = Anew[j, i] - A[j, i]

            cuda.syncthreads()

            # max-reduce err_sm vertically
            t = tpb // 2
            while t > 0:
                if ty < t:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty + t, tx])
                t //= 2
                cuda.syncthreads()

            # max-reduce err_sm horizontally
            t = tpb // 2
            while t > 0:
                if tx < t and ty == 0:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty, tx + t])
                t //= 2
                cuda.syncthreads()

            if tx == 0 and ty == 0:
                error[by, bx] = err_sm[0, 0]

        if config.ENABLE_CUDASIM:
            NN, NM = 4, 4
            iter_max = 20
        else:
            NN, NM = 256, 256
            iter_max = 1000

        A = np.zeros((NN, NM), dtype=np.float64)
        Anew = np.zeros((NN, NM), dtype=np.float64)

        n = NN

        tol = 1.0e-6
        error = 1.0

        for j in range(n):
            A[j, 0] = 1.0
            Anew[j, 0] = 1.0

        iter = 0

        blockdim = (tpb, tpb)
        griddim = (NN // blockdim[0], NM // blockdim[1])

        error_grid = np.zeros(griddim)

        stream = cuda.stream()

        dA = cuda.to_device(A, stream)          # to device and don't come back
        dAnew = cuda.to_device(Anew, stream)    # to device and don't come back
        derror_grid = cuda.to_device(error_grid, stream)

        while error > tol and iter < iter_max:
            self.assertTrue(error_grid.dtype == np.float64)

            jocabi_relax_core[griddim, blockdim, stream](dA, dAnew, derror_grid)

            derror_grid.copy_to_host(error_grid, stream=stream)

            # error_grid is available on host
            stream.synchronize()

            error = np.abs(error_grid).max()

            # swap dA and dAnew
            tmp = dA
            dA = dAnew
            dAnew = tmp

            iter += 1


if __name__ == '__main__':
    unittest.main()

87e43f3384abfa9763559d03e002ce07c385a9f9
<mask token>


class TestCudaLaplace(CUDATestCase):

    def test_laplace_small(self):

        @cuda.jit(float64(float64, float64), device=True, inline=True)
        def get_max(a, b):
            if a > b:
                return a
            else:
                return b

        @cuda.jit(void(float64[:, :], float64[:, :], float64[:, :]))
        def jocabi_relax_core(A, Anew, error):
            err_sm = cuda.shared.array(SM_SIZE, dtype=float64)
            ty = cuda.threadIdx.x
            tx = cuda.threadIdx.y
            bx = cuda.blockIdx.x
            by = cuda.blockIdx.y
            n = A.shape[0]
            m = A.shape[1]
            i, j = cuda.grid(2)
            err_sm[ty, tx] = 0
            if j >= 1 and j < n - 1 and i >= 1 and i < m - 1:
                Anew[j, i] = 0.25 * (A[j, i + 1] + A[j, i - 1] + A[j - 1, i
                    ] + A[j + 1, i])
                err_sm[ty, tx] = Anew[j, i] - A[j, i]
            cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if ty < t:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty + t, tx]
                        )
                t //= 2
                cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if tx < t and ty == 0:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty, tx + t]
                        )
                t //= 2
                cuda.syncthreads()
            if tx == 0 and ty == 0:
                error[by, bx] = err_sm[0, 0]
        if config.ENABLE_CUDASIM:
            NN, NM = 4, 4
            iter_max = 20
        else:
            NN, NM = 256, 256
            iter_max = 1000
        A = np.zeros((NN, NM), dtype=np.float64)
        Anew = np.zeros((NN, NM), dtype=np.float64)
        n = NN
        tol = 1e-06
        error = 1.0
        for j in range(n):
            A[j, 0] = 1.0
            Anew[j, 0] = 1.0
        iter = 0
        blockdim = tpb, tpb
        griddim = NN // blockdim[0], NM // blockdim[1]
        error_grid = np.zeros(griddim)
        stream = cuda.stream()
        dA = cuda.to_device(A, stream)
        dAnew = cuda.to_device(Anew, stream)
        derror_grid = cuda.to_device(error_grid, stream)
        while error > tol and iter < iter_max:
            self.assertTrue(error_grid.dtype == np.float64)
            jocabi_relax_core[griddim, blockdim, stream](dA, dAnew, derror_grid
                )
            derror_grid.copy_to_host(error_grid, stream=stream)
            stream.synchronize()
            error = np.abs(error_grid).max()
            tmp = dA
            dA = dAnew
            dAnew = tmp
            iter += 1


<mask token>

<mask token>
if config.ENABLE_CUDASIM:
    tpb = 4
else:
    tpb = 16
<mask token>


class TestCudaLaplace(CUDATestCase):

    def test_laplace_small(self):

        @cuda.jit(float64(float64, float64), device=True, inline=True)
        def get_max(a, b):
            if a > b:
                return a
            else:
                return b

        @cuda.jit(void(float64[:, :], float64[:, :], float64[:, :]))
        def jocabi_relax_core(A, Anew, error):
            err_sm = cuda.shared.array(SM_SIZE, dtype=float64)
            ty = cuda.threadIdx.x
            tx = cuda.threadIdx.y
            bx = cuda.blockIdx.x
            by = cuda.blockIdx.y
            n = A.shape[0]
            m = A.shape[1]
            i, j = cuda.grid(2)
            err_sm[ty, tx] = 0
            if j >= 1 and j < n - 1 and i >= 1 and i < m - 1:
                Anew[j, i] = 0.25 * (A[j, i + 1] + A[j, i - 1] + A[j - 1, i
                    ] + A[j + 1, i])
                err_sm[ty, tx] = Anew[j, i] - A[j, i]
            cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if ty < t:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty + t, tx]
                        )
                t //= 2
                cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if tx < t and ty == 0:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty, tx + t]
                        )
                t //= 2
                cuda.syncthreads()
            if tx == 0 and ty == 0:
                error[by, bx] = err_sm[0, 0]
        if config.ENABLE_CUDASIM:
            NN, NM = 4, 4
            iter_max = 20
        else:
            NN, NM = 256, 256
            iter_max = 1000
        A = np.zeros((NN, NM), dtype=np.float64)
        Anew = np.zeros((NN, NM), dtype=np.float64)
        n = NN
        tol = 1e-06
        error = 1.0
        for j in range(n):
            A[j, 0] = 1.0
            Anew[j, 0] = 1.0
        iter = 0
        blockdim = tpb, tpb
        griddim = NN // blockdim[0], NM // blockdim[1]
        error_grid = np.zeros(griddim)
        stream = cuda.stream()
        dA = cuda.to_device(A, stream)
        dAnew = cuda.to_device(Anew, stream)
        derror_grid = cuda.to_device(error_grid, stream)
        while error > tol and iter < iter_max:
            self.assertTrue(error_grid.dtype == np.float64)
            jocabi_relax_core[griddim, blockdim, stream](dA, dAnew, derror_grid
                )
            derror_grid.copy_to_host(error_grid, stream=stream)
            stream.synchronize()
            error = np.abs(error_grid).max()
            tmp = dA
            dA = dAnew
            dAnew = tmp
            iter += 1


if __name__ == '__main__':
    unittest.main()

<mask token>
if config.ENABLE_CUDASIM:
    tpb = 4
else:
    tpb = 16
SM_SIZE = tpb, tpb


class TestCudaLaplace(CUDATestCase):

    def test_laplace_small(self):

        @cuda.jit(float64(float64, float64), device=True, inline=True)
        def get_max(a, b):
            if a > b:
                return a
            else:
                return b

        @cuda.jit(void(float64[:, :], float64[:, :], float64[:, :]))
        def jocabi_relax_core(A, Anew, error):
            err_sm = cuda.shared.array(SM_SIZE, dtype=float64)
            ty = cuda.threadIdx.x
            tx = cuda.threadIdx.y
            bx = cuda.blockIdx.x
            by = cuda.blockIdx.y
            n = A.shape[0]
            m = A.shape[1]
            i, j = cuda.grid(2)
            err_sm[ty, tx] = 0
            if j >= 1 and j < n - 1 and i >= 1 and i < m - 1:
                Anew[j, i] = 0.25 * (A[j, i + 1] + A[j, i - 1] + A[j - 1, i
                    ] + A[j + 1, i])
                err_sm[ty, tx] = Anew[j, i] - A[j, i]
            cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if ty < t:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty + t, tx]
                        )
                t //= 2
                cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if tx < t and ty == 0:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty, tx + t]
                        )
                t //= 2
                cuda.syncthreads()
            if tx == 0 and ty == 0:
                error[by, bx] = err_sm[0, 0]
        if config.ENABLE_CUDASIM:
            NN, NM = 4, 4
            iter_max = 20
        else:
            NN, NM = 256, 256
            iter_max = 1000
        A = np.zeros((NN, NM), dtype=np.float64)
        Anew = np.zeros((NN, NM), dtype=np.float64)
        n = NN
        tol = 1e-06
        error = 1.0
        for j in range(n):
            A[j, 0] = 1.0
            Anew[j, 0] = 1.0
        iter = 0
        blockdim = tpb, tpb
        griddim = NN // blockdim[0], NM // blockdim[1]
        error_grid = np.zeros(griddim)
        stream = cuda.stream()
        dA = cuda.to_device(A, stream)
        dAnew = cuda.to_device(Anew, stream)
        derror_grid = cuda.to_device(error_grid, stream)
        while error > tol and iter < iter_max:
            self.assertTrue(error_grid.dtype == np.float64)
            jocabi_relax_core[griddim, blockdim, stream](dA, dAnew, derror_grid
                )
            derror_grid.copy_to_host(error_grid, stream=stream)
            stream.synchronize()
            error = np.abs(error_grid).max()
            tmp = dA
            dA = dAnew
            dAnew = tmp
            iter += 1


if __name__ == '__main__':
    unittest.main()

import numpy as np
from numba import cuda, float64, void
from numba.cuda.testing import unittest, CUDATestCase
from numba.core import config
if config.ENABLE_CUDASIM:
    tpb = 4
else:
    tpb = 16
SM_SIZE = tpb, tpb


class TestCudaLaplace(CUDATestCase):

    def test_laplace_small(self):

        @cuda.jit(float64(float64, float64), device=True, inline=True)
        def get_max(a, b):
            if a > b:
                return a
            else:
                return b

        @cuda.jit(void(float64[:, :], float64[:, :], float64[:, :]))
        def jocabi_relax_core(A, Anew, error):
            err_sm = cuda.shared.array(SM_SIZE, dtype=float64)
            ty = cuda.threadIdx.x
            tx = cuda.threadIdx.y
            bx = cuda.blockIdx.x
            by = cuda.blockIdx.y
            n = A.shape[0]
            m = A.shape[1]
            i, j = cuda.grid(2)
            err_sm[ty, tx] = 0
            if j >= 1 and j < n - 1 and i >= 1 and i < m - 1:
                Anew[j, i] = 0.25 * (A[j, i + 1] + A[j, i - 1] + A[j - 1, i
                    ] + A[j + 1, i])
                err_sm[ty, tx] = Anew[j, i] - A[j, i]
            cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if ty < t:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty + t, tx]
                        )
                t //= 2
                cuda.syncthreads()
            t = tpb // 2
            while t > 0:
                if tx < t and ty == 0:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty, tx + t]
                        )
                t //= 2
                cuda.syncthreads()
            if tx == 0 and ty == 0:
                error[by, bx] = err_sm[0, 0]
        if config.ENABLE_CUDASIM:
            NN, NM = 4, 4
            iter_max = 20
        else:
            NN, NM = 256, 256
            iter_max = 1000
        A = np.zeros((NN, NM), dtype=np.float64)
        Anew = np.zeros((NN, NM), dtype=np.float64)
        n = NN
        tol = 1e-06
        error = 1.0
        for j in range(n):
            A[j, 0] = 1.0
            Anew[j, 0] = 1.0
        iter = 0
        blockdim = tpb, tpb
        griddim = NN // blockdim[0], NM // blockdim[1]
        error_grid = np.zeros(griddim)
        stream = cuda.stream()
        dA = cuda.to_device(A, stream)
        dAnew = cuda.to_device(Anew, stream)
        derror_grid = cuda.to_device(error_grid, stream)
        while error > tol and iter < iter_max:
            self.assertTrue(error_grid.dtype == np.float64)
            jocabi_relax_core[griddim, blockdim, stream](dA, dAnew, derror_grid
                )
            derror_grid.copy_to_host(error_grid, stream=stream)
            stream.synchronize()
            error = np.abs(error_grid).max()
            tmp = dA
            dA = dAnew
            dAnew = tmp
            iter += 1


if __name__ == '__main__':
    unittest.main()

import numpy as np
from numba import cuda, float64, void
from numba.cuda.testing import unittest, CUDATestCase
from numba.core import config

# NOTE: CUDA kernel does not return any value

if config.ENABLE_CUDASIM:
    tpb = 4
else:
    tpb = 16
SM_SIZE = tpb, tpb


class TestCudaLaplace(CUDATestCase):
    def test_laplace_small(self):

        @cuda.jit(float64(float64, float64), device=True, inline=True)
        def get_max(a, b):
            if a > b:
                return a
            else:
                return b

        @cuda.jit(void(float64[:, :], float64[:, :], float64[:, :]))
        def jocabi_relax_core(A, Anew, error):
            err_sm = cuda.shared.array(SM_SIZE, dtype=float64)

            ty = cuda.threadIdx.x
            tx = cuda.threadIdx.y
            bx = cuda.blockIdx.x
            by = cuda.blockIdx.y

            n = A.shape[0]
            m = A.shape[1]

            i, j = cuda.grid(2)

            err_sm[ty, tx] = 0
            if j >= 1 and j < n - 1 and i >= 1 and i < m - 1:
                Anew[j, i] = 0.25 * ( A[j, i + 1] + A[j, i - 1]
                                      + A[j - 1, i] + A[j + 1, i])
                err_sm[ty, tx] = Anew[j, i] - A[j, i]

            cuda.syncthreads()

            # max-reduce err_sm vertically
            t = tpb // 2
            while t > 0:
                if ty < t:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty + t, tx])
                t //= 2
                cuda.syncthreads()

            # max-reduce err_sm horizontally
            t = tpb // 2
            while t > 0:
                if tx < t and ty == 0:
                    err_sm[ty, tx] = get_max(err_sm[ty, tx], err_sm[ty, tx + t])
                t //= 2
                cuda.syncthreads()

            if tx == 0 and ty == 0:
                error[by, bx] = err_sm[0, 0]

        if config.ENABLE_CUDASIM:
            NN, NM = 4, 4
            iter_max = 20
        else:
            NN, NM = 256, 256
            iter_max = 1000

        A = np.zeros((NN, NM), dtype=np.float64)
        Anew = np.zeros((NN, NM), dtype=np.float64)

        n = NN

        tol = 1.0e-6
        error = 1.0

        for j in range(n):
            A[j, 0] = 1.0
            Anew[j, 0] = 1.0

        iter = 0

        blockdim = (tpb, tpb)
        griddim = (NN // blockdim[0], NM // blockdim[1])

        error_grid = np.zeros(griddim)

        stream = cuda.stream()

        dA = cuda.to_device(A, stream)          # to device and don't come back
        dAnew = cuda.to_device(Anew, stream)    # to device and don't come back
        derror_grid = cuda.to_device(error_grid, stream)

        while error > tol and iter < iter_max:
            self.assertTrue(error_grid.dtype == np.float64)

            jocabi_relax_core[griddim, blockdim, stream](dA, dAnew, derror_grid)

            derror_grid.copy_to_host(error_grid, stream=stream)

            # error_grid is available on host
            stream.synchronize()

            error = np.abs(error_grid).max()

            # swap dA and dAnew
            tmp = dA
            dA = dAnew
            dAnew = tmp

            iter += 1


if __name__ == '__main__':
    unittest.main()
