CuPBoP/examples/dwt2d/dwt_cuda/rdwt97.cu

364 lines
15 KiB
Plaintext
Executable File

///
/// @file rdwt97.cu
/// @brief CUDA implementation of reverse 9/7 2D DWT.
/// @author Martin Jirman (207962@mail.muni.cz)
/// @date 2011-02-03 21:59
///
///
/// Copyright (c) 2011 Martin Jirman
/// All rights reserved.
///
/// Redistribution and use in source and binary forms, with or without
/// modification, are permitted provided that the following conditions are met:
///
/// * Redistributions of source code must retain the above copyright
/// notice, this list of conditions and the following disclaimer.
/// * Redistributions in binary form must reproduce the above copyright
/// notice, this list of conditions and the following disclaimer in the
/// documentation and/or other materials provided with the distribution.
///
/// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
/// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
/// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
/// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
/// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
/// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
/// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
/// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
/// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
/// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
/// POSSIBILITY OF SUCH DAMAGE.
///
#include "common.h"
#include "transform_buffer.h"
#include "io.h"
namespace dwt_cuda {
/// Wraps shared memory buffer and methods for computing 9/7 RDWT using
/// lifting schema and sliding window.
/// @tparam WIN_SIZE_X width of the sliding window
/// @tparam WIN_SIZE_Y height of the sliding window
template <int WIN_SIZE_X, int WIN_SIZE_Y>
class RDWT97 {
private:
/// Info related to loading of one input column.
/// @tparam CHECKED true if boundary chould be checked,
/// false if there is no near boudnary
template <bool CHECKED>
struct RDWT97Column {
/// laoder of input pxels for given column.
VerticalDWTBandLoader<float, CHECKED> loader;
/// Offset of loaded column in shared memory buffer.
int offset;
/// Sets all fields to some values to avoid 'uninitialized' warnings.
__device__ void clear() {
loader.clear();
offset = 0;
}
};
/// Shared memory buffer used for 9/7 DWT transforms.
typedef TransformBuffer<float, WIN_SIZE_X, WIN_SIZE_Y + 7, 4> RDWT97Buffer;
/// Shared buffer used for reverse 9/7 DWT.
RDWT97Buffer buffer;
/// Difference between indices of two vertical neighbors in buffer.
enum { STRIDE = RDWT97Buffer::VERTICAL_STRIDE };
/// Horizontal 9/7 RDWT on specified lines of transform buffer.
/// @param lines number of lines to be transformed
/// @param firstLine index of the first line to be transformed
__device__ void horizontalRDWT97(int lines, int firstLine) {
__syncthreads();
buffer.scaleHorizontal(scale97Mul, scale97Div, firstLine, lines);
__syncthreads();
buffer.forEachHorizontalEven(firstLine, lines, AddScaledSum(r97update2));
__syncthreads();
buffer.forEachHorizontalOdd(firstLine, lines, AddScaledSum(r97predict2));
__syncthreads();
buffer.forEachHorizontalEven(firstLine, lines, AddScaledSum(r97update1));
__syncthreads();
buffer.forEachHorizontalOdd(firstLine, lines, AddScaledSum(r97Predict1));
__syncthreads();
}
/// Initializes one column of shared transform buffer with 7 input pixels.
/// Those 7 pixels will not be transformed. Also initializes given loader.
/// @tparam CHECKED true if there are near image boundaries
/// @param colIndex index of column in shared transform buffer
/// @param input input image
/// @param sizeX width of the input image
/// @param sizeY height of the input image
/// @param column (uninitialized) info about loading one column
/// @param firstY index of first image row to be transformed
template <bool CHECKED>
__device__ void initColumn(const int colIndex, const float * const input,
const int sizeX, const int sizeY,
RDWT97Column<CHECKED> & column,
const int firstY) {
// coordinates of the first coefficient to be loaded
const int firstX = blockIdx.x * WIN_SIZE_X + colIndex;
// offset of the column with index 'colIndex' in the transform buffer
column.offset = buffer.getColumnOffset(colIndex);
if(blockIdx.y == 0) {
// topmost block - apply mirroring rules when loading first 7 rows
column.loader.init(sizeX, sizeY, firstX, firstY);
// load pixels in mirrored way
buffer[column.offset + 3 * STRIDE] = column.loader.loadLowFrom(input);
buffer[column.offset + 4 * STRIDE] =
buffer[column.offset + 2 * STRIDE] = column.loader.loadHighFrom(input);
buffer[column.offset + 5 * STRIDE] =
buffer[column.offset + 1 * STRIDE] = column.loader.loadLowFrom(input);
buffer[column.offset + 6 * STRIDE] =
buffer[column.offset + 0 * STRIDE] = column.loader.loadHighFrom(input);
} else {
// non-topmost row - regular loading:
column.loader.init(sizeX, sizeY, firstX, firstY - 3);
buffer[column.offset + 0 * STRIDE] = column.loader.loadHighFrom(input);
buffer[column.offset + 1 * STRIDE] = column.loader.loadLowFrom(input);
buffer[column.offset + 2 * STRIDE] = column.loader.loadHighFrom(input);
buffer[column.offset + 3 * STRIDE] = column.loader.loadLowFrom(input);
buffer[column.offset + 4 * STRIDE] = column.loader.loadHighFrom(input);
buffer[column.offset + 5 * STRIDE] = column.loader.loadLowFrom(input);
buffer[column.offset + 6 * STRIDE] = column.loader.loadHighFrom(input);
}
// Now, the next coefficient, which will be loaded by loader, is #4.
}
/// Using given loader, it loads another WIN_SIZE_Y coefficients
/// into specified column.
/// @tparam CHECKED true if there are near image boundaries
/// @param col info about loaded column
/// @param input buffer with input coefficients
template <bool CHECKED>
inline __device__ void loadWindowIntoColumn(RDWT97Column<CHECKED> & col,
const float * const input) {
for(int i = 7; i < (7 + WIN_SIZE_Y); i += 2) {
buffer[col.offset + i * STRIDE] = col.loader.loadLowFrom(input);
buffer[col.offset + (i + 1) * STRIDE] = col.loader.loadHighFrom(input);
}
}
/// Actual GPU 9/7 RDWT sliding window lifting schema implementation.
/// @tparam CHECKED_LOADS true if loader should check boundaries
/// @tparam CHECKED_WRITES true if boundaries should be taken into account
/// when writing into output buffer
/// @param in input image (9/7 transformed coefficients)
/// @param out output buffer (for reverse transformed image)
/// @param sizeX width of the output image
/// @param sizeY height of the output image
/// @param winSteps number of steps of sliding window
template <bool CHECKED_LOADS, bool CHECKED_WRITES>
__device__ void transform(const float * const in, float * const out,
const int sizeX, const int sizeY,
const int winSteps) {
// info about one main column and one boundary column
RDWT97Column<CHECKED_LOADS> column;
RDWT97Column<CHECKED_LOADS> boundaryColumn;
// index of first image row to be transformed
const int firstY = blockIdx.y * WIN_SIZE_Y * winSteps;
// initialize boundary columns
boundaryColumn.clear();
if(threadIdx.x < 7) {
// each thread among first 7 ones gets index of one of boundary columns
const int colId = threadIdx.x + ((threadIdx.x < 4) ? WIN_SIZE_X : -7);
// Thread initializes offset of the boundary column (in shared
// buffer), first 7 pixels of the column and a loader for this column.
initColumn(colId, in, sizeX, sizeY, boundaryColumn, firstY);
}
// All threads initialize central columns.
initColumn(parityIdx<WIN_SIZE_X>(), in, sizeX, sizeY, column, firstY);
// horizontally transform first 7 rows
horizontalRDWT97(7, 0);
// writer of output pixels - initialize it
const int outputX = blockIdx.x * WIN_SIZE_X + threadIdx.x;
VerticalDWTPixelWriter<float, CHECKED_WRITES> writer;
writer.init(sizeX, sizeY, outputX, firstY);
// offset of column (in transform buffer) saved by this thread
const int outColumnOffset = buffer.getColumnOffset(threadIdx.x);
// (Each iteration assumes that first 7 rows of transform buffer are
// already loaded with horizontally transformed pixels.)
for(int w = 0; w < winSteps; w++) {
// Load another WIN_SIZE_Y lines of this thread's column
// into the transform buffer.
loadWindowIntoColumn(column, in);
// possibly load boundary columns
if(threadIdx.x < 7) {
loadWindowIntoColumn(boundaryColumn, in);
}
// horizontally transform all newly loaded lines
horizontalRDWT97(WIN_SIZE_Y, 7);
// Using 7 registers, remember current values of last 7 rows
// of transform buffer. These rows are transformed horizontally
// only and will be used in next iteration.
float last7Lines[7];
for(int i = 0; i < 7; i++) {
last7Lines[i] = buffer[outColumnOffset + (WIN_SIZE_Y + i) * STRIDE];
}
// vertically transform all central columns
buffer.scaleVertical(scale97Div, scale97Mul, outColumnOffset,
WIN_SIZE_Y + 7, 0);
buffer.forEachVerticalOdd(outColumnOffset, AddScaledSum(r97update2));
buffer.forEachVerticalEven(outColumnOffset, AddScaledSum(r97predict2));
buffer.forEachVerticalOdd(outColumnOffset, AddScaledSum(r97update1));
buffer.forEachVerticalEven(outColumnOffset, AddScaledSum(r97Predict1));
// Save all results of current window. Results are in transform buffer
// at rows from #3 to #(3 + WIN_SIZE_Y). Other rows are invalid now.
// (They only served as a boundary for vertical RDWT.)
for(int i = 3; i < (3 + WIN_SIZE_Y); i++) {
writer.writeInto(out, buffer[outColumnOffset + i * STRIDE]);
}
// Use last 7 remembered lines as first 7 lines for next iteration.
// As expected, these lines are already horizontally transformed.
for(int i = 0; i < 7; i++) {
buffer[outColumnOffset + i * STRIDE] = last7Lines[i];
}
// Wait for all writing threads before proceeding to loading new
// coeficients in next iteration. (Not to overwrite those which
// are not written yet.)
__syncthreads();
}
}
public:
/// Main GPU 9/7 RDWT entry point.
/// @param in input image (9/7 transformed coefficients)
/// @param out output buffer (for reverse transformed image)
/// @param sizeX width of the output image
/// @param sizeY height of the output image
__device__ static void run(const float * const input, float * const output,
const int sx, const int sy, const int steps) {
// prepare instance with buffer in shared memory
__shared__ RDWT97<WIN_SIZE_X, WIN_SIZE_Y> rdwt97;
// Compute limits of this threadblock's block of pixels and use them to
// determine, whether this threadblock will have to deal with boundary.
// (3 in next expressions is for radius of impulse response of 9/7 RDWT.)
const int maxX = (blockIdx.x + 1) * WIN_SIZE_X + 3;
const int maxY = (blockIdx.y + 1) * WIN_SIZE_Y * steps + 3;
const bool atRightBoudary = maxX >= sx;
const bool atBottomBoudary = maxY >= sy;
// Select specialized version of code according to distance of this
// threadblock's pixels from image boundary.
if(atBottomBoudary) {
// near bottom boundary => check both writing and reading
rdwt97.transform<true, true>(input, output, sx, sy, steps);
} else if(atRightBoudary) {
// near right boundary only => check writing only
rdwt97.transform<false, true>(input, output, sx, sy, steps);
} else {
// no nearby boundary => check nothing
rdwt97.transform<false, false>(input, output, sx, sy, steps);
}
}
}; // end of class RDWT97
/// Main GPU 9/7 RDWT entry point.
/// @param in input image (9/7 transformed coefficients)
/// @param out output buffer (for reverse transformed image)
/// @param sizeX width of the output image
/// @param sizeY height of the output image
template <int WIN_SX, int WIN_SY>
__launch_bounds__(WIN_SX, CTMIN(SHM_SIZE/sizeof(RDWT97<WIN_SX, WIN_SY>), 8))
__global__ void rdwt97Kernel(const float * const in, float * const out,
const int sx, const int sy, const int steps) {
RDWT97<WIN_SX, WIN_SY>::run(in, out, sx, sy, steps);
}
/// Only computes optimal number of sliding window steps,
/// number of threadblocks and then lanches the 9/7 RDWT kernel.
/// @tparam WIN_SX width of sliding window
/// @tparam WIN_SY height of sliding window
/// @param in input image
/// @param out output buffer
/// @param sx width of the input image
/// @param sy height of the input image
template <int WIN_SX, int WIN_SY>
void launchRDWT97Kernel (float * in, float * out, int sx, int sy) {
// compute optimal number of steps of each sliding window
const int steps = divRndUp(sy, 15 * WIN_SY);
// prepare grid size
dim3 gSize(divRndUp(sx, WIN_SX), divRndUp(sy, WIN_SY * steps));
// finally launch kernel
PERF_BEGIN
rdwt97Kernel<WIN_SX, WIN_SY><<<gSize, WIN_SX>>>(in, out, sx, sy, steps);
PERF_END(" RDWT97", sx, sy)
CudaDWTTester::checkLastKernelCall("RDWT 9/7 kernel");
}
/// Reverse 9/7 2D DWT. See common rules (dwt.h) for more details.
/// @param in Input DWT coefficients. Format described in common rules.
/// Will not be preserved (will be overwritten).
/// @param out output buffer on GPU - will contain original image
/// in normalized range [-0.5, 0.5].
/// @param sizeX width of input image (in pixels)
/// @param sizeY height of input image (in pixels)
/// @param levels number of recursive DWT levels
void rdwt97(float * in, float * out, int sizeX, int sizeY, int levels) {
if(levels > 1) {
// let this function recursively reverse transform deeper levels first
const int llSizeX = divRndUp(sizeX, 2);
const int llSizeY = divRndUp(sizeY, 2);
rdwt97(in, out, llSizeX, llSizeY, levels - 1);
// copy reverse transformed LL band from output back into the input
memCopy(in, out, llSizeX, llSizeY);
}
// select right width of kernel for the size of the image
if(sizeX >= 960) {
launchRDWT97Kernel<192, 8>(in, out, sizeX, sizeY);
} else if (sizeX >= 480) {
launchRDWT97Kernel<128, 6>(in, out, sizeX, sizeY);
} else {
launchRDWT97Kernel<64, 6>(in, out, sizeX, sizeY);
}
}
} // end of namespace dwt_cuda